demos/fnn/nn_in_keras.ipynb

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   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Humboldt-WI/adams/blob/master/demos/feedforward_networks/nn_in_keras.ipynb) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing Neural Networks using Keras "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having build a simple from-scratch NN in the [previus demo](https://github.com/Humboldt-WI/adams/blob/master/demos/feedforward_networks/nn_foundations.ipynb), we are ready to examine the the implementation of deep learning models in Keras. [Keras](https://keras.io/) is one of many programming frameworks for deep learning. As usual, different tools or frameworks have advantages and disadvantages. For Keras, the main advantage is simplicity. It is great for learning and will suffice for many applied deep learning research projects. \n",
    "\n",
    "Keras is a high-level API, meaning that you need a partnering tool, a deep learning workhorse if you wish, that handles the actual computations. Keras supports multiple alternatives but the natural choice is Google's [Tensorflow](https://www.tensorflow.org/). Tensorflow is one of the most popular and powerful deep learning frameworks. In a nutshell, Tensorflow allows you to do everything you can do in Keras and a lot more. However, the downside is that programming in tensorflow is a little more complicated, which makes Keras the better choice for this course. For the sake of completeless, note that other popular and powerful frameworks for deep learning include [PyTorch](https://pytorch.org/) and [MXNet](https://mxnet.apache.org/), to name just a few. Further, an interesting option similar to Keras is [Gluon](https://aws.amazon.com/de/blogs/aws/introducing-gluon-a-new-library-for-machine-learning-from-aws-and-microsoft/), a joint venture of Amazon AWS and Microsoft. Gluon is similar to Keras in that it offers developers an easy way to interface an underlying deep learning framework; in this case MXNet. Gluon is also the framework used in our main text book [Dive into Deep Learning](http://d2l.ai/). And here is just [another GREAT resource](https://gluon.mxnet.io/index.html) for learning about deep learning and Gluon.      \n",
    "\n",
    "If you are interested in learning a little more about the different options, [this post](https://towardsdatascience.com/top-10-best-deep-learning-frameworks-in-2019-5ccb90ea6de) is a good starting point. \n",
    "\n",
    "Enough talking about libraries, frameworks and the like. Let's start with Keras. Our goal is to build a NN for multi-class classification. We need to make several decisions when configuring our NN and focus on these when learning about Keras. Here is the agenda for this demo. \n",
    "\n",
    "**Outline**\n",
    "\n",
    " 1. number of hidden layers\n",
    " 2. number of neurons in hidden layers\n",
    " 3. activation functions\n",
    " 4. type of weight initialization\n",
    " 5. loss function\n",
    " 6. learning rate and momentum\n",
    " 6. optimizer\n",
    " 7. size of batch\n",
    " 8. number of epochs\n",
    " 9. regularization (dropout)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use case: app rating analysis \n",
    "NNs often solve classification problems with many classes. We will see many examples when coming to the NLP part of the course. To illustrate  a multi-class context that, we will a demo data set concerned with app ratings. The original data is available on [Kaggle](https://www.kaggle.com/ramamet4/app-store-apple-data-set-10k-apps). There, you also find a data dictionary with some information on the variables. However, the variable names are rather self-explanatory. A cleaned version of the data is our repo. If you want to examine the preprocessing steps, you can check out the script ([app_store_dpp.py](https://github.com/Humboldt-WI/adams/tree/master/demos/feedforward_networks/app_store_dpp.py)), which starts from the original Kaggle data set and goes through a couple of pre-processing operations to produce the version we use below. "
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       "\n",
       "                                                    rating_count_tot  \\\n",
       "track_name                                                             \n",
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       "Bible                                                      12.847931   \n",
       "\n",
       "                                                    sup_devices.num  \\\n",
       "track_name                                                            \n",
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       "Bible                                                     -0.096808   \n",
       "\n",
       "                                                    ipadSc_urls.num  lang.num  \\\n",
       "track_name                                                                      \n",
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       "track_name                                                                   \n",
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       "track_name                                                            \n",
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       "Bible                                                             0  ...   \n",
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       "track_name                                                                      \n",
       "PAC-MAN Premium                                                             0   \n",
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       "Bible                                                                       0   \n",
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       "track_name                                                                     \n",
       "PAC-MAN Premium                                                            0   \n",
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       "track_name                                                                  \n",
       "PAC-MAN Premium                                                         0   \n",
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       "track_name                                                                 \n",
       "PAC-MAN Premium                                                        0   \n",
       "Evernote - stay organized                                              0   \n",
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       "Bible                                                                  0   \n",
       "\n",
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       "track_name                                                                          \n",
       "PAC-MAN Premium                                                                 0   \n",
       "Evernote - stay organized                                                       0   \n",
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       "track_name                                                               \n",
       "PAC-MAN Premium                                                      0   \n",
       "Evernote - stay organized                                            0   \n",
       "WeatherBug - Local Weather, Radar, Maps, Alerts                      0   \n",
       "eBay: Best App to Buy, Sell, Save! Online Shopping                   0   \n",
       "Bible                                                                0   \n",
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       "track_name                                                               \n",
       "PAC-MAN Premium                                                      0   \n",
       "Evernote - stay organized                                            0   \n",
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       "track_name                                                                  \n",
       "PAC-MAN Premium                                                         0   \n",
       "Evernote - stay organized                                               0   \n",
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       "track_name                                                                \n",
       "PAC-MAN Premium                                                       0   \n",
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       "                                                    user_rating  \n",
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   ],
   "source": [
    "# Setting up the environment: import standard libraries and load the data. \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import np_utils\n",
    "\n",
    "# Load dataset and set app name as index\n",
    "data_file = 'AppleStore_prep.csv' \n",
    "X = pd.read_csv(data_file, index_col=False, sep='\\t', encoding='utf-8')\n",
    "X = X.set_index('track_name')\n",
    "X.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target variable in this data set is the user rating of the app."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show target distribution\n",
    "plt.hist(X.user_rating)\n",
    "plt.xlabel('user_rating')\n",
    "plt.title('Rating distribution')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train a NN predict this multi-class target, we have to properly encode the target variable. Specifically, we encode it as a dummy vector as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dummy encoding of the target\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(X[\"user_rating\"])\n",
    "y = np_utils.to_categorical( encoder.transform(X[\"user_rating\"]))\n",
    "X.pop(\"user_rating\")\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5037, 35)\n",
      "(5037, 6)\n"
     ]
    }
   ],
   "source": [
    "# Split data into training and test set\n",
    "seed=888\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=seed)\n",
    "    \n",
    "print (X_train.shape)\n",
    "print(y_train.shape) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Keras framework \n",
    "\n",
    "### 1+2: Number of hidden layers and number of neurons\n",
    "The number of layers and the number of neurons is a parameter that you would have to decide on. \n",
    "\n",
    "A *deep* NN is one with more than one hidden layer, so we will use **2 hidden layers** this time.\n",
    "\n",
    "As to the size of hidden layers, we go for **n=128**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_units = 128"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3: activation function ###\n",
    "We will go for industry standard which is **relu** and keep a **softmax** at the end, given our multiclass task. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4: weight initialisation\n",
    "Weight initialization might seem minor but it can have a major impact on the convergence of the network.\n",
    "\n",
    "At the picture: all weights are set to 0, second is the normally distributed weights with std=0.4. \n",
    "In the rightmost plot, the weights are drawn from normal distributions with variances which are inversely proportional to the number of inputs into each neuron (he normal).\n",
    "\n",
    "<img src=\"https://intoli.com/blog/neural-network-initialization/img/training-losses.png\"  alt=\"fwe\" style=\"width: 1000px;\"/> \n",
    "\n",
    "https://intoli.com/blog/neural-network-initialization/\n",
    "\n",
    "We will be using the **He Normal initialization**, which is one of the industry standards and recommended for ReLu. It draws samples from a truncated normal distribution with $\\mu=0$ and $ \\sigma = \\sqrt{2 / (d_{l-1})}$. Recall that $d_{l-1}$ refers to the number of nodes in the previous layer and, therefore, the number of inputs to a node in the current layer.\n",
    "\n",
    "So in our case the standard deviation for our first weight matrix would be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23904572186687872"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you remember we previously used the (6/(35)) - we used the Glorot uniform (also called Xavier initialization)\n",
    "std = np.sqrt(2/(X.shape[1])) \n",
    "std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's put down what we have decided on already: ##\n",
    "Keras offer you two \"styles\" of setting up the structure of your network (they call it two types of model): **sequential** and **functional API** (https://keras.io/models/about-keras-mod). In general, the code is a little easier to read when using the sequential API, whereas the functional API allows for some more flexibility in configuring the network. Since this is the first demo of Keras, we illustrate both options.  Later, we will focus on the sequential API. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_12 (Dense)            (None, 128)               4608      \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 6)                 774       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 21,894\n",
      "Trainable params: 21,894\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "model = keras.Sequential()\n",
    "model.add(layers.Dense(nb_units,  #number of neurons\n",
    "                activation='relu',  #activation function\n",
    "                kernel_initializer=keras.initializers.he_normal(seed=seed),  #initialisation of weights; check doc for more options\n",
    "                bias_initializer='zeros', #initialization of biases, will also be done by default\n",
    "                input_shape=(X.shape[1],))) #first hidden layer size, i.e. number of features\n",
    "# One more layer with the same configuration\n",
    "model.add(layers.Dense(nb_units, activation='relu', kernel_initializer=keras.initializers.he_normal(seed=seed), bias_initializer='zeros')) \n",
    "\n",
    "# And finally the outout layer\n",
    "nb_classes = y.shape[1]\n",
    "model.add(layers.Dense(nb_classes, activation='softmax', kernel_initializer=keras.initializers.he_normal(seed=seed), bias_initializer='zeros')) \n",
    "\n",
    "######################let's see how our architecture looks like (also very helpful to learn how many parameters will be trained and compare to\n",
    "#the size of your training set)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to confirm these parameters manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4608\n",
      "16512\n",
      "768\n"
     ]
    }
   ],
   "source": [
    "# input to hidden layer:\n",
    "\n",
    "print(X.shape[1] * nb_units + nb_units)\n",
    "\n",
    "# first hidden to second hidden layer\n",
    "print(nb_units * nb_units + nb_units)\n",
    "\n",
    "# second hidden to output layer\n",
    "print(nb_units * nb_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 35)]              0         \n",
      "                                                                 \n",
      " dense_104 (Dense)           (None, 128)               4608      \n",
      "                                                                 \n",
      " dense_105 (Dense)           (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_106 (Dense)           (None, 6)                 774       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 21,894\n",
      "Trainable params: 21,894\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Functional API analogue\n",
    "#from keras.layers import Input, Dense\n",
    "#from keras.models import Model\n",
    "\n",
    "inputs = layers.Input(shape=(X.shape[1],))\n",
    "layer1 = layers.Dense(nb_units,  activation='relu', #activation function\n",
    "                kernel_initializer=keras.initializers.he_normal(seed=seed), #initialisation of weights, \n",
    "                bias_initializer='zeros')(inputs)\n",
    "layer2 = layers.Dense(nb_units,activation='relu',kernel_initializer=keras.initializers.he_normal(seed=seed),bias_initializer='zeros')(layer1)\n",
    "predictions = layers.Dense(nb_classes, activation='softmax')(layer2)\n",
    "\n",
    "model = keras.Model(inputs=inputs, outputs=predictions)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wonderful, we have set up the infrastructure and can proceed with training\n",
    "Most of the parameters that define the training process are set within the **compile** and **fit** methods.\n",
    "\n",
    "The parameter optimization strategy is one of the most important choices for the training of the neural network.\n",
    "\n",
    "We have already learned about one - stochastic gradient descent, it is considered the basic one. Improved performance can be achieved by using optimizers like **Rmsprop, Adagrad,  Adam**, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5+6: loss function, learning rate and optimizer\n",
    "As discussed before, the loss function is defined by the task (mse, binary cross-entropy, etc.), in our case it is cross-entropy.\n",
    "Learning rate is set in the optimizer but does not have to remain the same throughout the training (i.e. AdaGrad)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use SGD as optimizer and cover more options later on\n",
    "opt = keras.optimizers.SGD(learning_rate=0.1) # there are many more parameters that we could be adding here\n",
    "\n",
    "# Calling compile and specifying some mandatory arguments completes setting up the NN\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7+8: batch size and epochs ###\n",
    "The batch size determines how many examples you look at before making a weight update. Put differently, we approximate the gradient of the loss function with respect to the weights based on the error signal from considering *batch size* data points. The lower the batch size, the noisier the training signal is going to be, the higher it is, the longer it will take to compute the gradient for each weight update.\n",
    "\n",
    "Epochs is the number of rounds the algorithm will go through the entire dataset. Usually the more epochs of training, the better are the results, until the network starts to overfit. When you see the results flattening out, it's the time to stop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "90/90 [==============================] - 1s 4ms/step - loss: 1.0170 - accuracy: 0.6885 - val_loss: 0.9629 - val_accuracy: 0.7023\n",
      "Epoch 2/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9530 - accuracy: 0.7056 - val_loss: 0.9433 - val_accuracy: 0.7069\n",
      "Epoch 3/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9327 - accuracy: 0.7052 - val_loss: 0.9338 - val_accuracy: 0.7046\n",
      "Epoch 4/30\n",
      "90/90 [==============================] - 0s 2ms/step - loss: 0.9221 - accuracy: 0.7070 - val_loss: 0.9423 - val_accuracy: 0.7069\n",
      "Epoch 5/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9157 - accuracy: 0.7092 - val_loss: 0.9346 - val_accuracy: 0.7046\n",
      "Epoch 6/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9054 - accuracy: 0.7105 - val_loss: 0.9304 - val_accuracy: 0.7079\n",
      "Epoch 7/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8980 - accuracy: 0.7101 - val_loss: 0.9223 - val_accuracy: 0.7069\n",
      "Epoch 8/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8914 - accuracy: 0.7107 - val_loss: 0.9181 - val_accuracy: 0.7120\n",
      "Epoch 9/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8809 - accuracy: 0.7129 - val_loss: 0.9264 - val_accuracy: 0.7120\n",
      "Epoch 10/30\n",
      "90/90 [==============================] - 0s 2ms/step - loss: 0.8738 - accuracy: 0.7135 - val_loss: 0.9238 - val_accuracy: 0.7056\n",
      "Epoch 11/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8699 - accuracy: 0.7155 - val_loss: 0.9169 - val_accuracy: 0.7102\n",
      "Epoch 12/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8639 - accuracy: 0.7165 - val_loss: 0.9320 - val_accuracy: 0.7088\n",
      "Epoch 13/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8636 - accuracy: 0.7165 - val_loss: 0.9226 - val_accuracy: 0.7134\n",
      "Epoch 14/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8573 - accuracy: 0.7191 - val_loss: 0.9179 - val_accuracy: 0.7097\n",
      "Epoch 15/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8519 - accuracy: 0.7195 - val_loss: 0.9354 - val_accuracy: 0.7139\n",
      "Epoch 16/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8497 - accuracy: 0.7201 - val_loss: 0.9172 - val_accuracy: 0.7051\n",
      "Epoch 17/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8405 - accuracy: 0.7189 - val_loss: 0.9238 - val_accuracy: 0.7069\n",
      "Epoch 18/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8437 - accuracy: 0.7209 - val_loss: 0.9172 - val_accuracy: 0.7106\n",
      "Epoch 19/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8379 - accuracy: 0.7171 - val_loss: 0.9225 - val_accuracy: 0.7009\n",
      "Epoch 20/30\n",
      "90/90 [==============================] - 0s 2ms/step - loss: 0.8346 - accuracy: 0.7203 - val_loss: 0.9297 - val_accuracy: 0.7019\n",
      "Epoch 21/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8268 - accuracy: 0.7252 - val_loss: 0.9216 - val_accuracy: 0.7032\n",
      "Epoch 22/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8318 - accuracy: 0.7219 - val_loss: 0.9169 - val_accuracy: 0.7093\n",
      "Epoch 23/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8227 - accuracy: 0.7258 - val_loss: 0.9207 - val_accuracy: 0.7116\n",
      "Epoch 24/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8201 - accuracy: 0.7232 - val_loss: 0.9127 - val_accuracy: 0.7088\n",
      "Epoch 25/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8186 - accuracy: 0.7248 - val_loss: 0.9224 - val_accuracy: 0.7116\n",
      "Epoch 26/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8156 - accuracy: 0.7248 - val_loss: 0.9163 - val_accuracy: 0.7097\n",
      "Epoch 27/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8103 - accuracy: 0.7262 - val_loss: 0.9178 - val_accuracy: 0.7116\n",
      "Epoch 28/30\n",
      "90/90 [==============================] - 0s 2ms/step - loss: 0.8141 - accuracy: 0.7221 - val_loss: 0.9226 - val_accuracy: 0.7069\n",
      "Epoch 29/30\n",
      "90/90 [==============================] - 0s 2ms/step - loss: 0.8054 - accuracy: 0.7278 - val_loss: 0.9301 - val_accuracy: 0.6954\n",
      "Epoch 30/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8047 - accuracy: 0.7250 - val_loss: 0.9445 - val_accuracy: 0.7056\n"
     ]
    }
   ],
   "source": [
    "batch_size = 56 #1 for online SGD\n",
    "epochs = 30\n",
    "\n",
    "#And now we fit the model (that is the training part), mind that saving it to the \n",
    "# variable will help you retrieve and analyse the training history\n",
    "story = model.fit(X_train, y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1,\n",
    "                    validation_data=(X_test, y_test) #OR validation_split=0.3\n",
    "                 ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output already provides a lot of information on how well our model performs and whether is shows tendencies of overfitting. You can also obtain such insight explicitly after the model has trained. For example, you can use *evaluate()* to compute model performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.9444984197616577\n",
      "Test accuracy: 0.7055555582046509\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The metrics that *evaluate()* gives us depend on the input argument *metrics* that we used when setting up our NN. Keras knows several other metrics (see [Keras doc. for details](https://keras.io/metrics/)) but not all known metrics are supported for training the NN. Between, you can also extend the set of metrics with customer metrics (see this [example](https://machinelearningmastery.com/custom-metrics-deep-learning-keras-python/) if interested.).\n",
    "\n",
    "We also have access to model performance results via the result of *model.fit()*, that is our variable *story*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "story.history.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is good practice to define custom functions to diagnose model performance and display results as needed. Here are some examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_history(story):\n",
    "    plt.plot(story.history['accuracy'])\n",
    "    plt.plot(story.history['val_accuracy'])\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train_accuracy', 'test_accuracy'], loc='best')\n",
    "    plt.show()\n",
    "\n",
    "show_history(story)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_history(story):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "    fig.set_size_inches(18.5, 10.5)\n",
    "    ax1.plot(story.history['accuracy'])\n",
    "    ax1.plot(story.history['val_accuracy'])\n",
    "    ax1.set(xlabel='epoch', ylabel='accuracy')\n",
    "    ax1.legend(['train_accuracy', 'test_accuracy'], loc='best')\n",
    "    ax1.set_title('Accuracy evolution during NN training')\n",
    "    \n",
    "    ax2.plot(story.history['loss'])\n",
    "    ax2.plot(story.history['val_loss'])\n",
    "    ax2.set(xlabel='epoch', ylabel='loss')\n",
    "    ax2.legend(['train_loss', 'test_loss'], loc='best')\n",
    "    ax2.set_title('Loss evolution during NN training')\n",
    "    plt.show()\n",
    "\n",
    "show_history(story)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get more insight and benefit from the full stack of performance measures that you know, we can simply calculate test set predictions explicitly and then proceed with these in the usual way, producing ROC curves, confusion tables, and the like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2160, 6)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute predictions\n",
    "yhat = model.predict(X_test)\n",
    "yhat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 4 0 ... 4 4 4]\n"
     ]
    }
   ],
   "source": [
    "# Finding the most probable class\n",
    "yhat_c = np.argmax(yhat, axis=1)\n",
    "print(yhat_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "     Grade 0       0.43      0.41      0.42       268\n",
      "     Grade 1       0.00      0.00      0.00        15\n",
      "     Grade 2       0.18      0.02      0.03       105\n",
      "     Grade 3       0.00      0.00      0.00       116\n",
      "     Grade 4       0.75      0.94      0.83      1506\n",
      "     Grade 5       0.00      0.00      0.00       150\n",
      "\n",
      "    accuracy                           0.71      2160\n",
      "   macro avg       0.23      0.23      0.21      2160\n",
      "weighted avg       0.58      0.71      0.63      2160\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "target_names = [\"Grade 0\",\"Grade 1\",\"Grade 2\",\"Grade 3\",\"Grade 4\",\"Grade 5\"]\n",
    "true_label = np.argmax(y_test,axis = 1)\n",
    "print ('\\nClasification report:\\n', classification_report(true_label, yhat_c, target_names=target_names, zero_division=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion matrix:\n",
      " [[ 109    0    2    0  157    0]\n",
      " [   6    0    0    0    9    0]\n",
      " [  19    0    2    0   83    1]\n",
      " [  15    0    0    0  101    0]\n",
      " [  87    0    5    1 1413    0]\n",
      " [  15    0    2    1  132    0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print ('\\nConfusion matrix:\\n', confusion_matrix(true_label, yhat_c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9: dropout\n",
    "Dropout 'impedes' the model and can be used against overfitting. It randomly sets a fraction rate of input units to 0 at each update during training time, which is a manner of regularization. Let's add up the Dropout rates after the hidden layers.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_35\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_107 (Dense)           (None, 128)               4608      \n",
      "                                                                 \n",
      " dropout_62 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_108 (Dense)           (None, 128)               16512     \n",
      "                                                                 \n",
      " dropout_63 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_109 (Dense)           (None, 6)                 774       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 21,894\n",
      "Trainable params: 21,894\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Let's add dropout layers to our model (we can leave the bias setting to default this time)\n",
    "model = keras.Sequential()\n",
    "model.add(layers.Dense(nb_units, activation='relu',bias_initializer='zeros',kernel_initializer=keras.initializers.he_normal(seed=seed),  input_shape=(35,)))\n",
    "model.add(layers.Dropout(0.1))\n",
    "\n",
    "model.add(layers.Dense(nb_units, activation='relu',bias_initializer='zeros',kernel_initializer=keras.initializers.he_normal(seed=seed)))\n",
    "model.add(layers.Dropout(0.1))\n",
    "model.add(layers.Dense(nb_classes, activation='softmax',kernel_initializer=keras.initializers.he_normal(seed=seed)))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) #nothing changes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "90/90 [==============================] - 1s 5ms/step - loss: 1.0413 - accuracy: 0.6897 - val_loss: 0.9622 - val_accuracy: 0.7009\n",
      "Epoch 2/30\n",
      "90/90 [==============================] - 0s 4ms/step - loss: 0.9743 - accuracy: 0.6998 - val_loss: 0.9476 - val_accuracy: 0.7079\n",
      "Epoch 3/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9562 - accuracy: 0.7002 - val_loss: 0.9370 - val_accuracy: 0.7093\n",
      "Epoch 4/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9430 - accuracy: 0.7068 - val_loss: 0.9360 - val_accuracy: 0.7079\n",
      "Epoch 5/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9385 - accuracy: 0.7042 - val_loss: 0.9337 - val_accuracy: 0.7083\n",
      "Epoch 6/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9338 - accuracy: 0.7066 - val_loss: 0.9290 - val_accuracy: 0.7069\n",
      "Epoch 7/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9190 - accuracy: 0.7072 - val_loss: 0.9229 - val_accuracy: 0.7065\n",
      "Epoch 8/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9210 - accuracy: 0.7078 - val_loss: 0.9245 - val_accuracy: 0.7134\n",
      "Epoch 9/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9125 - accuracy: 0.7078 - val_loss: 0.9287 - val_accuracy: 0.7106\n",
      "Epoch 10/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.9032 - accuracy: 0.7090 - val_loss: 0.9199 - val_accuracy: 0.7056\n",
      "Epoch 11/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8999 - accuracy: 0.7099 - val_loss: 0.9209 - val_accuracy: 0.7097\n",
      "Epoch 12/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8999 - accuracy: 0.7086 - val_loss: 0.9282 - val_accuracy: 0.7120\n",
      "Epoch 13/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8961 - accuracy: 0.7105 - val_loss: 0.9159 - val_accuracy: 0.7111\n",
      "Epoch 14/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8965 - accuracy: 0.7099 - val_loss: 0.9145 - val_accuracy: 0.7074\n",
      "Epoch 15/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8914 - accuracy: 0.7088 - val_loss: 0.9261 - val_accuracy: 0.7130\n",
      "Epoch 16/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8834 - accuracy: 0.7123 - val_loss: 0.9158 - val_accuracy: 0.7111\n",
      "Epoch 17/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8790 - accuracy: 0.7117 - val_loss: 0.9189 - val_accuracy: 0.7111\n",
      "Epoch 18/30\n",
      "90/90 [==============================] - 0s 4ms/step - loss: 0.8807 - accuracy: 0.7139 - val_loss: 0.9103 - val_accuracy: 0.7116\n",
      "Epoch 19/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8747 - accuracy: 0.7145 - val_loss: 0.9118 - val_accuracy: 0.7116\n",
      "Epoch 20/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8774 - accuracy: 0.7143 - val_loss: 0.9191 - val_accuracy: 0.7093\n",
      "Epoch 21/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8736 - accuracy: 0.7151 - val_loss: 0.9089 - val_accuracy: 0.7074\n",
      "Epoch 22/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8782 - accuracy: 0.7145 - val_loss: 0.9112 - val_accuracy: 0.7139\n",
      "Epoch 23/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8695 - accuracy: 0.7173 - val_loss: 0.9097 - val_accuracy: 0.7116\n",
      "Epoch 24/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8656 - accuracy: 0.7187 - val_loss: 0.9115 - val_accuracy: 0.7116\n",
      "Epoch 25/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8648 - accuracy: 0.7173 - val_loss: 0.9073 - val_accuracy: 0.7125\n",
      "Epoch 26/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8651 - accuracy: 0.7181 - val_loss: 0.9071 - val_accuracy: 0.7144\n",
      "Epoch 27/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8621 - accuracy: 0.7127 - val_loss: 0.9074 - val_accuracy: 0.7134\n",
      "Epoch 28/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8662 - accuracy: 0.7185 - val_loss: 0.9097 - val_accuracy: 0.7088\n",
      "Epoch 29/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8584 - accuracy: 0.7181 - val_loss: 0.9106 - val_accuracy: 0.7120\n",
      "Epoch 30/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8575 - accuracy: 0.7151 - val_loss: 0.9136 - val_accuracy: 0.7106\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.9135689735412598\n",
      "Test accuracy: 0.7106481194496155\n"
     ]
    }
   ],
   "source": [
    "story = model.fit(X_train, y_train, \n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1,\n",
    "                    validation_data=(X_test, y_test))#there is a better solution to it\n",
    "score = model.evaluate(X_test, y_test, verbose=0)\n",
    "show_history(story)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### more on optimizers\n",
    "\n",
    "So far we have mainly been looking at the SGD as our optimization algorithm, but there are many more: Adam, RMSprop, Adagrad, etc. (some more info here http://ruder.io/optimizing-gradient-descent/). Like other tools they all have their strengths and weaknesses. We will look more into Adam:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_40\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_122 (Dense)           (None, 128)               4608      \n",
      "                                                                 \n",
      " dropout_72 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_123 (Dense)           (None, 128)               16512     \n",
      "                                                                 \n",
      " dropout_73 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_124 (Dense)           (None, 6)                 774       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 21,894\n",
      "Trainable params: 21,894\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(layers.Dense(nb_units, activation='relu',bias_initializer='zeros',kernel_initializer=keras.initializers.he_normal(seed=seed), input_shape=(35,)))\n",
    "model.add(layers.Dropout(0.1))\n",
    "model.add(layers.Dense(nb_units, activation='relu',bias_initializer='zeros',kernel_initializer=keras.initializers.he_normal(seed=seed)))\n",
    "model.add(layers.Dropout(0.1))\n",
    "model.add(layers.Dense(nb_classes, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=keras.optimizers.Adam(learning_rate=0.05),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "story = model.fit(X_train, y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=60,\n",
    "                    verbose=0,\n",
    "                    validation_data=(X_test, y_test))\n",
    "score = model.evaluate(X_test, y_test, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BBMmNYATKHZiTXEEQBGFhuRUjZtg/nwT+EfO5vBPYhckpszvCbAHuBCKYTiZf1lrfg7kg8lnMuUw/JlS+1GT/I5jzkD9a5Z13Ypy0QPbi0B8wk/Mf5ty/F+Oa/U/rcV6KCVpNFHmM72Cyyg5jvpMLv9/+GSMChZRSxQLSHZ37VCLnO++zmO+8LZjyaHv5HdbYdgKPYEpta8mbMCHsI5jX8Ic4/L7VWo9jvqvLnW8cwhzrcuctX8dk04SU1YmxyH6eAv4f5nUfALaSc5zmkbmeswlCFmVK6ARBWGyUUh8HTtVav7niysKCo0y7va9qrddXXPkkxHIU9QBvsk6yBUEQBEGYB5RSPwT2aK3LtfE9aZFzNmEuiINEEJYAVknO24EbF3ssgkEpVaeUulYp5VFKrcaUTN2y2ONaSiilrlZKBS0rtp178sdFHpYgCIIgnFAopS5USm1SSrmssu+XUdnZfNIg52xCLRGBRBAWGaXUOzHhq7dpre9b7PEIWRSmJncMY9d8GlO/LUzzbEwXKNsi/XJdup2yIAiCIAizYyUmYy4CfBF4j9b6sUUd0dJCztmEmiElNoIgCIIgCIIgCIIgnPSIg0QQBEEQBEEQBEEQhJMeEUgEQRAEQRAEQRAEQTjp8Sz2ABaCjo4OvWHDhsUehiAIgiAsex555JFhrXXnYo/jZEPOZQRBEAShNpQ7lzkpBJINGzawY8eOxR6GIAiCICx7lFJHFnsMJyNyLiMIgiAItaHcuYyU2AiCIAiCIAiCIAiCcNIjAokgCIIgCIIgCIIgCCc9IpAIgiAIgiAIgiAIgnDSc1JkkBQjmUzS09NDLBZb7KEIDggEAqxZswav17vYQxEEQRAEQRAEQZgXZJ5aO2YzhzxpBZKenh6amprYsGEDSqnFHo5QBq01IyMj9PT0sHHjxsUejiAIgiAIgiAIwrwg89TaMNs55ElbYhOLxWhvb5c33TJAKUV7e7uoqIIgCIIgCIIgnNDIPLU2zHYOedIKJIC86ZYR8loJgiAIgiAIgnAyIHOf2jCb43hSCySCIAiCIAiCIAiCIAggAsmiEQqF+PKXv1z1dtdeey2hUKj2AxIEQRAEQRAEQRBOehZ6rnr99ddz8803V73dfCACySJR6k2XSqXKbnfrrbcSDAbnaVRzp9L4BUEQBEEQBEEQhKXLiTpXdYIIJIvEDTfcwMGDBznnnHO48MILueyyy7juuus488wzAXj5y1/O+eefz1lnncWNN96Y3W7Dhg0MDw9z+PBhzjjjDN75zndy1llncdVVVxGNRks+3te+9jUuvPBCtm/fzqte9SqmpqYAGBgY4BWveAXbt29n+/btPPDAAwB8+9vfZtu2bWzfvp23vOUtwExlr7GxEYB7773X8fh//etfc95557F9+3Yuv/xyMpkMW7ZsYWhoCIBMJsPmzZuzfwuCIAiCIAiCIAgLx0LPVXO56667OPfcc9m6dSt/+qd/Sjwez47pzDPPZNu2bXzoQx8C4Mc//jFnn30227dv57nPfW5NnvtJ2+Y3l0/94kme6h2v6T7PXNXMJ156Vsnln/3sZ9m9ezePP/449957Ly9+8YvZvXt3tgXRN77xDdra2ohGo1x44YW86lWvor29PW8f+/fv5/vf/z5f+9rXeO1rX8tPfvIT3vzmNxd9vFe+8pW8853vBOBjH/sYX//613nf+97HX/7lX/K85z2PW265hXQ6TSQS4cknn+Qf//EfeeCBB+jo6GB0dLTi83300Ucrjj+TyfDOd76T++67j40bNzI6OorL5eLNb34z3/3ud/nABz7AnXfeyfbt2+ns7HR0nAVBEARBEARBEE5EFmOeCgs/V7WJxWJcf/313HXXXZx66qn8yZ/8CV/5yld4y1vewi233MKePXtQSmXLeD796U/zm9/8htWrV9cshkIcJEuEiy66KK8/8xe/+EW2b9/Os571LI4dO8b+/ftnbLNx40bOOeccAM4//3wOHz5ccv+7d+/msssuY+vWrXz3u9/lySefBODuu+/mPe95DwBut5uWlhbuvvtuXvOa19DR0QFAW1tbTcb/xz/+kec+97nZ9ez9/umf/inf/va3AfPP9ra3va3i4wmCIAiCIAiCIAjzz3zPVW327t3Lxo0bOfXUUwF461vfyn333UdLSwuBQIC3v/3t/PSnP6W+vh6ASy+9lOuvv56vfe1rpNPpuT9RxEECUFFBWwgaGhqyv997773ceeed/OEPf6C+vp7nP//5Rfs3+/3+7O9ut7usben666/nZz/7Gdu3b+eb3/wm9957b9Vj9Hg8ZDIZwJTCJBKJOY3fZu3ataxYsYK7776bhx56iO9+97tVj00QBEEQBEEQBOFEYinMU2H+56qV8Hg8PPTQQ9x1113cfPPNfOlLX+Luu+/mq1/9Kg8++CC/+tWvOP/883nkkUdmOFmqRRwki0RTUxMTExNFl4XDYVpbW6mvr2fPnj388Y9/nPPjTUxM0N3dTTKZzBMgLr/8cr7yla8AkE6nCYfDvPCFL+THP/4xIyMjANkSmw0bNvDII48A8POf/5xkMlnV+J/1rGdx3333cejQobz9ArzjHe/gzW9+M695zWtwu91zfr6CIAiCIAiCIAhC9Sz0XNXmtNNO4/Dhwxw4cACA73znOzzvec8jEokQDoe59tpr+cIXvsATTzwBwMGDB7n44ov59Kc/TWdnJ8eOHZvzGMRBski0t7dz6aWXcvbZZ1NXV8eKFSuyy6655hq++tWvcsYZZ3DaaafxrGc9a86P9w//8A9cfPHFdHZ2cvHFF2ff8P/xH//Bu971Lr7+9a/jdrv5yle+wrOf/Wz+7u/+juc973m43W7OPfdcvvnNb/LOd76Tl73sZWzfvp1rrrkmT0nMpdT4Ozs7ufHGG3nlK19JJpOhq6uLO+64A4DrrruOt73tbVJeIwiCIAiCIAiCsIgs9FzVJhAIcNNNN/Ga17yGVCrFhRdeyLvf/W5GR0d52cteRiwWQ2vN5z//eQA+/OEPs3//frTWXH755Wzfvn3OY1Ba6znvZKlzwQUX6B07duTd9/TTT3PGGWcs0oiEQnbs2MFf/dVf8bvf/a7kOvKaCYIgLD5KqUe01hcs9jhONoqdywiCIAgnHjLnqS3Fjme5cxlxkAiLzmc/+1m+8pWvSPaIIAiCIAiCIAiCsGiIQHKC8Rd/8Rf8/ve/z7vv/e9//5IuXbnhhhu44YYbFnsYgiAIgiAIgiAIwjyxHOaqIpCcYPzXf/3XYg9BEARBEARBEARBEPJYDnNV6WIjCIIgCIIgCIIgCMJJjwgkgiAIgiAIgiAIgiCc9IhAIgiCIAiCIAiCIAjCSY8IJItEKBTiy1/+8qy2/fd//3empqZqPCJBEIQThHQKbnk3DO1d7JEIwpLlO384zKu+8sBiD0MQBEEQlhQikCwSJ4pAkkqlFnsIgiAI+Uz0whPfh4N3L/ZIBGHJMjAe5/FjocUehiAIgrAEme+56oYNGxgeHp7V/ucbEUgWiRtuuIGDBw9yzjnn8OEPf5h/+7d/48ILL2Tbtm184hOfAGBycpIXv/jFbN++nbPPPpsf/vCHfPGLX6S3t5cXvOAFvOAFLyi5//e85z1ccMEFnHXWWdn9ATz88MNccsklbN++nYsuuoiJiQnS6TQf+tCHOPvss9m2bRv/+Z//CeS/cXfs2MHzn/98AD75yU/ylre8hUsvvZS3vOUtHD58mMsuu4zzzjuP8847jwcemL4i9S//8i9s3bqV7du3Z5/zeeedl12+f//+vL8FQRDmTDJmbmPjizsOQVjCuF2KdEajtV7soQiCIAhLjBPlYv5skDa/ALfdAP27arvPlVvhRZ8tufizn/0su3fv5vHHH+f222/n5ptv5qGHHkJrzXXXXcd9993H0NAQq1at4le/+hUA4XCYlpYWPv/5z3PPPffQ0dFRcv+f+cxnaGtrI51Oc/nll7Nz505OP/10Xve61/HDH/6QCy+8kPHxcerq6rjxxhs5fPgwjz/+OB6Ph9HR0YpP76mnnuL++++nrq6Oqakp7rjjDgKBAPv37+cNb3gDO3bs4LbbbuP//u//ePDBB6mvr2d0dJS2tjZaWlp4/PHHOeecc7jpppuWVN9rQRBOAFJRcxsXgUQQSuF1KwBSGZ39XRAEQVhiLMI8FfIv5l955ZV0dXXxox/9iHg8zite8Qo+9alPMTk5yWtf+1p6enpIp9P8/d//PQMDA9mL+R0dHdxzzz0Vh/P5z3+eb3zjGwC84x3v4AMf+EDRfb/uda/jhhtu4Oc//zkej4errrqKz33uczU5JLmIQLIEuP3227n99ts599xzAYhEIuzfv5/LLruMv/7rv+YjH/kIL3nJS7jssssc7/NHP/oRN954I6lUir6+Pp566imUUnR3d3PhhRcC0NzcDMCdd97Ju9/9bjwe83Zoa2uruP/rrruOuro6AJLJJO9973t5/PHHcbvd7Nu3L7vft73tbdTX1+ft9x3veAc33XQTn//85/nhD3/IQw895Ph5CYIgVMR2kIhAIgglcbuMiTiV1njdizwYQRAEYUkx3xfzbR555BFuuukmHnzwQbTWXHzxxTzvec/jmWeembHvkZERbrnlFvbs2YNSilAoNC/PXQQSqKigzTdaaz760Y/yZ3/2ZzOWPfroo9x666187GMf4/LLL+fjH/94xf0dOnSIz33uczz88MO0trZy/fXXE4vFqh6Xx+Mhk8kAzNi+oaEh+/sXvvAFVqxYwRNPPEEmkyEQCJTd76te9So+9alP8cIXvpDzzz+f9vb2qscmCIJQkqyDZGJxxyEISxiPy3aQZABRSARBEJYkizxPhfm5mG9z//3384pXvCI7t3zlK1/J7373O6655poZ+06lUgQCAd7+9rfzkpe8hJe85CU1fZ42kkGySDQ1NTExYU7er776ar7xjW8QiUQAOH78OIODg/T29lJfX8+b3/xmPvzhD/Poo4/O2LYY4+PjNDQ00NLSwsDAALfddhsAp512Gn19fTz88MMATExMkEqluPLKK/nv//7vbOCqXWKzYcMGHnnkEQB+8pOflHy8cDhMd3c3LpeL73znO6TTaQCuvPJKbrrppmwNmr3fQCDA1VdfzXve8x4prxEEofZIBokgVMRjldWkM5JBIgiCIJTGvpj/+OOP8/jjj3PgwAHe/va3c+qpp/Loo4+ydetWPvaxj/HpT3+6Zo9ZbN8ej4eHHnqIV7/61fzyl7/kmmuuqdnj5SICySLR3t7OpZdeytlnn80dd9zBG9/4Rp797GezdetWXv3qVzMxMcGuXbu46KKLOOecc/jUpz7Fxz72MQDe9a53cc0115QMad2+fTvnnnsup59+Om984xu59NJLAfD5fPzwhz/kfe97H9u3b+fKK68kFovxjne8g3Xr1rFt2za2b9/O9773PQA+8YlP8P73v58LLrgAt7v01aU///M/51vf+hbbt29nz549WQXwmmuu4brrruOCCy7gnHPOyasRe9Ob3oTL5eKqq66qyfEUBEHIIg4SQaiI7SBJpkUgEQRBEPKZz4v5uVx22WX87Gc/Y2pqisnJSW655RYuu+yyovuORCKEw2GuvfZavvCFL/DEE0/My3NXJ0N6+QUXXKB37NiRd9/TTz/NGWecsUgjEj73uc8RDof5h3/4B8fbyGsmCIIjHv8+/Ozd0Hk6/MWDiz2aEw6l1CNa6wsWexwnG8XOZebC9x86ykd/uos/fvRyVraUL40VBEEQFo6lMud54xvfyM6dO3nRi17EmjVr+J//+R8AGhsb+d///V8OHDjAhz/8YVwuF16vl6985StccMEF/Od//idf+tKXWLVqVcmQ1g0bNrBjxw46OjqKhrT+5je/mbHv1atX87KXvYxYLIbWmg996EO89a1vrfg8ih3PcucykkEiLDiveMUrOHjwIHffffdiD0UQhBORpNVaThwkglASd14GiSAIgiDkY1cV2Lz//e/P+3vTpk1cffXVM7Z73/vex/ve976y+z58+HD29w9+8IN88IMfzFt+9dVXF933QjT3EIFkmXPxxRcTj8fz7vvOd77D1q1bF2lElbnlllsWewiCIJzIpCSDRBAqkQ1plRIbQRAEQcgiAsky58EHxT4uCIKQR9LKIElMQCYDLonbEoRCPG6rza+EtAqCIAjzxHK8mC8CiSAIgnBikcppS56YgEDL4o1FEJYoHimxEQRBEOaZ5Xgx/6S+rHYyBNSeKMhrJQiCY2wHCUgOiSCUQEpsBEEQli4y96kNszmOJ61AEggEGBkZkTffMkBrzcjICIGApOwLguCAXAeJ5JAIQlE8biOQpKXERhAEYUkh89TaMNs55ElbYrNmzRp6enoYGhpa7KEIDggEAqxZs2axhyEIwnIgmSOQiINEEIridtkZJFJiIwiCsJSQeWrtmM0c8qQVSLxeLxs3blzsYQiCIAi1JpVbYiMOEkEohldKbARBEJYkMk9dXE7aEhtBEAThBCUZA5fX/C4CiSAUxZ0NaRWBRBAEQRBsRCARBEEQTixSUWjoNL9LBokgFMXOIBGBRBAEQRCmEYFEEARBOLFIxqCxy/xebQbJ0Qdh1821H9NCozXc/HbYe9tij0RYonisDJK0ZJAIgiAIQhYRSARBEIQTi1QU6tsBVX2JzR+/DHd8Yl6GtaBM9MPumyHcs9gjOWFRSn1DKTWolNpdYvnpSqk/KKXiSqkPFSw7rJTapZR6XCm1Y2FGnI9dYpOUDBJBEARByCICiSAIgnBikYyBtw78zdU7SGKhE6PzzdAec9tx6uKO48Tmm8A1ZZaPAn8JfK7E8hdorc/RWl9Q64E5weu2HSQikAiCIAiCjQgkgiAIwolFKmoEkkBz9Rkk0RAkJkyJynJmeJ+57TxtccdxAqO1vg8jgpRaPqi1fhhILtyonCMhrYIgCIIwExFIBEEQhBOLZAw8AfA3VV9iEwuBzkAyWnHVJc3QXgi0QOOKxR6JUBwN3K6UekQp9a7FGIAn2+ZXMkgEQRAEwcaz2AMQBEEQhJpiO0j8zdULJNExc5uIgK++9mNbKIb2QsdpoNRij0QoznO01seVUl3AHUqpPZYjJQ9LPHkXwLp162o6AOliIwiCIAgzEQeJIAiCcGKR5yCpIk8kk5kuyZlNDsnYkaVTmjO8V8prljBa6+PW7SBwC3BRifVu1FpfoLW+oLOzs6ZjsLvYpCSkVRAEQRCyiEAiCIIgnDhk0pCOg7e++gySeBhT+YBxkFTD2GH4j+1w6LfVbTcfTI3C5JAIJEsUpVSDUqrJ/h24CijaCWc+sR0k0uZXEARBEKaREhtBEAThxCEVM7feWThIoqHp36t1kIz3Adq0111shvaa2w4RSOYTpdT3gecDHUqpHuATgBdAa/1VpdRKYAfQDGSUUh8AzgQ6gFuUKX/yAN/TWv96ocfvkZBWQRAEQZiBCCSCIAjCiUPSEkg8s8ggiYWmf49X6SCxBZXEZHXbzQfDlkAiDpJ5RWv9hgrL+4E1RRaNA9vnZVBVkO1iIyU2giAIgpBFSmwEQRAWiT//7iN8+d4Diz2ME4uU1X3GGzACSSoGqYSzbXMdJNWW2NhCzFLofjO015QYtaxd7JEISxiv28ogEQeJIAiCIGQRB4kgCMIicd++Ybl6W2tyHSSBZvN7fAI87ZW3zXOQVFliYwsqS0Ug6dgCLrkGIpTGLW1+BUEQBGEGcvYkCIKwCEzEkkTiKSLx1GIP5cQiz0HSZH53WmYzJweJJagkl0CJjd3iVxDKIBkkgiAIgjATcZAIgiAsAv1h43SYFIGktuQ6SJTb/O5UIKlFBsliO0jiERjvgc5TF3ccwpJHKYXbpUiLQCIIgiAIWebVQaKUukYptVcpdUApdUOR5V9QSj1u/exTSoVylv1aKRVSSv2yYJtvKqUO5Wx3znw+B0EQhPmgzxJIJkQgqS1FHSQOy2WiIXB5wddYfYlNViCZqm67WjO8z9x2nr644xCWBW6XIiltfgVBEAQhy7w5SJRSbuC/gCuBHuBhpdTPtdZP2etorf8qZ/33Aefm7OLfgHrgz4rs/sNa65vnZeCCIAgLQF/YTOTFQVJjch0kbusrLlaFg6QuaJwniWoFEusxEosskEiLX6EKvC5FWnKQBEEQBCHLfDpILgIOaK2f0VongB8ALyuz/huA79t/aK3vAqo8QxUEQVge2A6SSEwEkppS2MUGqnOQBILgb5xFic0SCWkd3mtcMG0bF3ccwrLA7VKSQSIIgiAIOcynQLIaOJbzd4913wyUUuuBjcDdDvf9GaXUTqtExz+3YQqCICw82QySRJqMTFBqR66DJCuQVOkg8TXOIaR1CThI2jeB27u44xCWBR63i5SU2AiCIAhClqXSxeb1wM1a67SDdT8KnA5cCLQBHym2klLqXUqpHUqpHUNDQ7UbqSAIQg2wHSQAkwlxkdSMuXaxCQTNdrMOaV0CAkmHBLQKzvBISKsgCIIg5DGfAslxYG3O32us+4rxenLKa8qhte7ThjhwE6aUp9h6N2qtL9BaX9DZ2VnFsAVBOGHZdztEBhd7FMC0gwSQVr+1JNdB4g2A21d9Bom/aXmGtKbiMHZIAloFx3hciqRkkAiCIAhClvkUSB4GtiilNiqlfBgR5OeFKymlTgdagT842alSqtu6VcDLgd21GrAgCCcw6SR8//Xw4FcXeySACWlta/ABEtRaU3IdJFCd2BENQV2rVWIzS4FkMUNaRw6AzkCnBLQKzvC4XeIgEQRBEIQc5k0g0VqngPcCvwGeBn6ktX5SKfVppdR1Oau+HviB1jrvG1op9Tvgx8DlSqkepdTV1qLvKqV2AbuADuAf5+s5CIJwApGcAp2GscOLPRIm4ynGYyk2dzUCMCFBrbUj10ECJofESYlNJgOx8OxDWm1BZTFDWu0ONiKQCA7xSEirIAiCIOQxb21+AbTWtwK3Ftz38YK/P1li28tK3P/CWo1PEISTCPvKfujo4o6D6fyRzV2NPHRolMm4k/glwRGpqCmrcVn6v1MHSXwc0KbEJhWrLqRV66VRYjO0F1DQvnnxxiAsK9wuRSotIa2CIAiCYLNUQloFQRDmF3viOnZkccfBdP7IFstBEoknF3M4JxbJGHjrpv8OtDgTSKJj1vpBI6qkYpB26OxJTpnSFuVeXIFkeC+0rs9//oJQBtPFRhwkgiAIgmAjAokgCCcHiUlzOzm4uGUQmPwRIFtiExEHSe1ITk2X14ARO5yEtMZC5tZu8wvOc0hsAaahE9IJ58JKrRnaJwGtQlV4xEEiCIIgCHmIQCIIwslBrigSOrZ442DaQZIVSGLiIKkZqdh0QCs4zyCJhsyt7SAB5+Gu9nqNXdYYFkGAS6dgZL+0+BWqwuOWDBJBEARByEUEEkEQTg6Sk9O/L3IOSd94jPYG33QXm4Q4SGpGMjrTQeJEIMl1kPgtB4nToNasQLLC3C5GJ5vQEeNekYBWoQo8LiVdbARBEAQhBxFIBEE4OcidtIYWN4ekLxRlZUsAv8eNz+2SLja1pNBBEmg2AoauMAnMdZD4LAeJ06BWWyBpsgSSxcghGdpjbqXERqgCE9IqAokgCIIg2IhAIgjCyUFyCQkk4RjdLWYS3+B3MxkXgaRmJGMzHSSZVOXcmaIOkmpLbGyBZBFKbOwWvx1bFv6xhWWL1+0ilZEMEkEQBEGwEYFEEISTA1sg8TYseolN/3iMlZZA0hjwEBGBpHakojMzSKCy2BENgcsL3vqckNZZltgshoNkeB80rTJdewTBIW4psREEQRCEPEQgEQTh5MAusek8bVEFkmgiTWgqSXeLcTk0+EQgqSkzHCS2QFIhhyQWMu4RpeaQQWKFtC5WiU2nBLQK1eFxKZJSYiMIgiAIWUQgEQTh5MAOae08fVEFkv5x08FmZbNxOTQFPEQkg6R2FDpIAg4FkmjI5I+Ac9eJTWKRS2y0Ni1+OySgVagOj8slDhJBEARByEEEEkEQTg4SU+DyQPsmmBxanE4jQF/YTJ67g3YGiYfJhAgkNaNYBglAzKGDBHJKbKrIIHH7pgWWxGTZ1WtOuMcIgNLBRqgSt1uRlAwSQRAEQcgiAokgCCcHyajJHwmuN38vkoukL2QcJHaJTaNfHCQ1ZS4ZJLbA4fEZwaOaEht/E/jqzd8L7SAZtgJaRSARqsQrGSSCIAiCkIcIJIIgnBwkJ8FbB8F15u9FEkgKS2wa/ZJBUlOSMfDkCiSWg8RpBomNr7G6kFZfowl4hYXPIBnaZ26lxa9QJW6XS9r8CoIgCEIOIpAIgnBykJgyV/hbbQfJ4rT67QtHCdZ7qfO5ARFIaorWloMkp8QmMAsHCZig1qocJM3Tj7vgAskeqGuDho6FfVxh2eNxKWnzKwiCIAg5iEAiCMLJgV1i09AFbv/iOUjCsax7BEwGyVQiLTb3WpCKm9tcB4nPQQZJJgOxcIGDpKk6B4m/KcdBstAlNvukvEaYFR63lNgIgiAIQi4ikAiCcHKQnDQOEpcLgmsX0UESY1Vw2uHQFPAASFBrLUhZwkSug8TtMcJYuRKb+DigCxwkTZXLcrLbWwKJy23Et4UMadXaavErAolQPdLmVxAEQRDyEYFEEAQYOwKTw4s9ivklMTU9cQ6uW1wHSUu+gwRgUsps5k7S5LvkOUigstgRC5nbutacbaotsbE63/jqF9ZBMjkM0TFp8SvMCo9b2vwKgiAIQi4ikAiCAN97HdzxicUexfySnDJOAlg0gSSWTDMymaA7p8Sm0RJIpJNNDcg6SOrz7w80l88giYbM7VxCWu0wWO8CCyTSwUaYA5JBIgiCIAj5iEAiCCc7mQyMHoTJocUeyfySnJpuwxpcD1Mjzh0CNWLA7mDTUkQgEQfJ3LEdJN4iDpJyGSS2g2ROIa22QFJnyrkWiqE95lYEEmEWuF1KutgIgiAIQg4ikAjCyc7UMKQTC995Y6FJTE07C+xWv+FjCzqEvrCZwHe3TGdkNAZEIKkZtoPEU5d/v382DhKHIa3plHlcv9UtZ6EdJEP7jNulefXCPaZwwuBxu0hlNFqLSCIIgiAIIAKJIAjhHnPrtJxguZLMFUisVr9jCxvU2h+e6SBp8EkGSc2whYliDpJyGSTRMXM7I6R1woSgliMxMb0+WAJJ7cTGTEYzVS7Ad2gPdJwKStXsMYWTB4/LvG8khkQQBEEQDCKQCMLJzvhxc5s4gR0kWpvOIr4CB8kC55BMO0imJ/B2F5sJySCZO9mQ1gIHSaUMkmxIa3D6Pn8joCt3pLH367NCWr11Nf1f+u5DR3nOv9xDLJkuvoK0+BXmgNsSSJJpySERBEEQBBCBRBCEsC2QLGBuwkKTToJOTztIGrtMp5MFbvXbH47SHPBkO9eAdLGpKalSDpLm8hkk0RC4vPnhrrbgUclZFS9wkPgaalpi8/jREKOTCZ7sDc9cGAvDRJ8IJMKs8bqNQCKdbARBEATBIAKJIJzsjFslNgsZLLnQ2M/NZ3WxUWpROtn0hmN5+SMADX43IBkkNaGUg8TfbEphSnXriIWMeyS3TMUWPCoFtRYKJN66mpbYHBo2j//Y0dDMhcP7za20+BVmidtlTgMlqFUQBEEQDCKQCMLJzsngILFLHrw5E+dFEEj6w7G8/BEAv8eNz+1iQgSSuVPSQWKJF4kSZTbRUH7+COQ4SMqU5kCOQGKHtNZWIDk8Yvb16NGxmQunRs1tY1fNHk84ubAdJNLqVxAEQRAMIpAIwsmOnUGSTphSlBMRe8LqbZi+L7huwUts+sKxvPwRm8aAR0psakG5DBIonUNiO0hy8VsCSdUOktqV2ISmEoxOJlCqhIPELv/xNcxcJggOsDNIpMRGEARBEAwikAjCyY7tIIET10ViCyS+nIyJ4DrTvaRcNkUNSaQyDEfiMxwkAI1+DxEJaZ07lRwkpV7rHAfJAweG+e6DR3JcJ84Ekpi7jn/45VNMam/NHCSHhs3/4yWb2ukLx+gLFwgv2fe1CCTC7LC72CRFIBEEQRAEQAQSQTi5yaRNyGNDp/n7RBVIsiU2uQKJ1eo3fGxBhjAwbtwNqwoySMAEtUbiJbqUCM4pl0ECjhwk3/rDYT758yeZ0IHy29hYy+88GOXr9x9i12ASMilIJaoffwGHR8z/4yvPXQMUcZHY/69eEUiE2eGxMkjSkkEiCIIgCIAIJIJwcjPRb7q72CGPNcxOWFLYIa3FBJIFyiHptwSSYg6SJr+HSPwELW9aSFJRcHnA7cm/PyuQVHaQDE7ESaY19x22xBaHAskvnjZdZp4etoSuGvwvHRqaxKXgmrNX4vO4eKwwhyRRED4sCFXicdsOEskgEQRBEAQQgUSYD5IxePC/jTtBWNqM95rbzlPNbaVyguVKokSJDSyYQNIbMuURxTJIGvxuJsVBMneSsZnuEcjJICkikGQypl2u5SAZmogD8Ku9ljDioMRG+xq4Z/8o9T43B8ZsgWTuOSSHRqZY01pPg9/D1tUtxR0kygUe/5wfSzg5yTpIpMRGEARBEAARSIT54MAdcNvfQN/jiz0SoRJ2i98OWyA5QUts7MlqroOkocP8PbYwQa394dIOksaAV9r81oJUdGb+CJTPIImPAxoCQbTWDE3EcbsUdx6MoFGVQ1oTE8RcDSRSGT545alMakusqIWDZDjCxg7jDjl3bZCdx8MkUjlX+hOTpttObntiQagCO6RV2vwKgiAIgkEEEqH2REPmNnGClmucSNgBrVmB5AR9zYqV2Ci1oJ1s+sIxGv0emgLeGcsa/e4lIZBEE+klMY5ZU8pBUi6DJBYyt3VBJuIp4qkMV525gkRak/LUO3KQhNN+upr8XH/JBrwB6z02R4FEa83h4alpgWRdK4lUhqf7ckSe5KSU1whzwg5plTa/giAIgmAQgUSoPTFTi08qtrjjECozftxcgW7qNn+fTCU2YAkkC5RBEo4VdY/A0uhik0xneN2Nf+DPvrNjUccxJ0o5SHwNphSlWImNLegGgtnymqvOWkF3S4AJXVc6tyT7kOMMJny86OyVeNwuTl2zwtwfn5sbaygSJxJPZQWS89YHAfJzSBKT+aKfIFSJnUGSkhIbQRAEQQBEIBHmA1sgqUENvjDPhHugeTX4G83fJ2xIq93FpuBq+wIKJH3jsaL5I2C62EST6UXNAfjv3x5kZ0+Yo6PL+D2QjIG3iINEKVNmU8FBYgskK5oCXHP2SsZSPpLR8iGt46FRxjMBXrTViIxnbzS3B3oGZ/00wAS0AmywBJLuljpWNgd4NDeHJDElDhJhTkgGiSAIgiDkIwKJUHvsCYc4SJY+48ehZfW0cHDCZpBMgds3s7tJcJ15v9qi3jzSF4qWFEga/WZci1Xesm9ggi/edQCXgtDkMu6mk4oWL7EBU2ZTLIPEdpDUtWYFks4mPy/e2s2EDjA6OlL2IaOREAlPIxduaANg68aVAOw+0j+rp2Bjt/g9pWNaADl3XZDHjuU6SCIikAhzItvFJi0lNoIgCIIAIpAI84E4SOZGMrpwxy583DhI7EnWiVxiU8xZsECdbJLpDEOROCtbik/ebYFkchEEklQ6w4d//ASNAQ9vedZ6JuKp/CDQ5UQyVrzEBoxAUqxcxhZ0A0EGcwSS89a1knQ3MDEeKvlwU4kUxCMEg23ZsMvGBpN3sv/YwGyfBQDPDE/ic7tYFZx+z5y3rpVjo9GskGNCWkUgEWaPnUEiDhJBEARBMIhAItQeySCZGz9+G/zkHfP/OKkERAagZY1pE6pcJ3ZIa2F5DUBwvbmdZ4FkcCKO1sVb/AI0BhbPQfI/9x/iiZ4wn7zuLDZ3mVKrUDSx4OOoCWUdJE3lM0isEhuvW9FS58XlUjQ1B0lFx0sKV/fuHaKBKbq7OqbvtDJBxsbDHA/NXug8PDzJuvb6rPACxkECOTkkSSmxEeaGdLERBEEQhHxEIBFqjzhI5sbgU/DMvZBJV7XZcCTO396yi2jC4XYTfYA2DhKlTFjrCVtiE50Z0AoLJpD0h83/QqmQ1oZFKrE5MBjh83fs46ozV/DSbd0E630AhKaWaZlNskRIK0CguXQGicsL3nqGJuJ0NvpRVtvcjvZ26nWUu/cUzxO5dWcvjSrKio7O6TstgaSORMntnHBoeDIb0Gpz9uoWPC7FY8dC5o5ECeFPEBzidZvTQAlpFQRBEASDCCRC7REHyezRGib6TanL4FNVbfrbvUN878GjPHJkrPLKYPJHwGSQgJnYJU9QgSQxVbzbR32bmWDOs0DSFzb/C6UcJE22QLKAnWzSGc3f3PwEdV43//iKs1FK0dZgBJLRyWXqICnV5heMg6RoBskY1AVBKYYicTqb/NlFbW0dNLti3Lqrb8ZmsWSaB/b24CGDq655eoFVyrWqPsM9sxRIMhnN4ZGpGQJJwOvmrFXN0w4SKbER5og7W2KzTMvqBEEQBKHGiEAi1B5xkMye6BikrXyBYw9WtWmf5VI4MupQ5AhbAkmzJZD4Gk5gB0mJdqhKmRySsSPz+vD9WYGk+OS9YREySG76/SEePRriEy89k64mI9wE670AhKaWqUBSqs0vWBkkRRwk0RAEggDGQZIjkLj8jTSqGPfsHTR5Izncu3cIt53Z42+aXmAJJKe3e/j9gWHnjq4cesNREqnMDIEE4Nx1rTxxLEwqnbEEEmnzK8weO4MkKSU2giAIggCIQCLMByKQzJ6JnCvVxx6qatPjITMJPzLiMEdkvMfcngwCSWKq9ERyAVr99oZi1PvcNAc8RZfbIa0TCySQHB6e5HO37+WFp3fxinNXZ+9vtUpsxpZtiU0FB0mpkNa6IGALJDkCi78Rj06STia4Z89Q3ma37upjVb0lfvhzHCRKgbeeU4Iu4qkMf3hmuOqncWjYavHbXkwgCRJNptnbZ4mpvsaq9y8INh63tPkVBEEQhFxEIBFqSzo13QklJQJJ1dgCSePKWTtIDg9X4SAJtIDfmmCdyAJJMlrcQQLQun7+M0jGo6xsCWSzLQpZyC42mYzmb36yE6/bxT+9YmvemGyBZFmW2Ghd3kESaDZlf6mC52Y5SNIZzehkvoMEn3GGrGtIc+vuafEylkxz19MDXHGKJcYUihTeOlbVZ6j3uWeVQ2L/D5/SOVMgOW9dKwC7D1njkRIbYQ5MO0ikxEYQBEEQQAQSodbkXqFNLpEMkmQMfvLOeS+jqAnj1qTnjJfC2GGYcN4qtNfqmHF01KmD5Dg0r5n+e7kIJMcfgV98wEyInZIsk9UQXAfx8HQ3k3mgLxwrmT8COSGttcggSUzBzX8KoWNFF3/nj0d46NAof//iM2eExtb53AS8ruVZYpNOgs6UcZBYLo/CMhvLQTISiZPR5Asklnh4zamN3P30YLZc5r59Q0wm0jx3vbVubokNgLcBdzrGpZs7uGfPELqa9yqmxW+9z01X7lgs1rTW0dHoY89R67OilPAnCA7wuKXNryAIgiDkIgKJUFtioenfl4qDZOQA7PoRHP7dYo+kMhP95vbMl5nbHudlNn05JTaOJmThnumAVrBCWpdBm9+nfwmP3DRdyuWExFQ2G2IGwXXmdh5dJP3hGCubSzw+4PO48HlcRBI1EEgGn4LdP4HD989YdGx0in/59R4u29LBay5YU2Rj4yJZliU29udNuQwSmFlmYzlIBidM9k9nY66DxAgkV57SQDSZ5rf7jBvktt39BOu9nNVufYXOEEjqIDnFC0/v4ngoyr6BSFVP5fDwJBvaG4o6jpRSnLO2lYPHB/PGKAizIdvmVwQSQRAEQQBEIBFqTe5V+KXiILFLfuLVTVIWhYk+qGuDtReB2+e4zGY8lmQinmJNax3RZJoha7JXfqPj0/kjsHza/EasiWGxPIlSJKdKt0PNCiTz4zBKpTMMTsTLOkjAdLKpiYNk0sq8KBCQtNZ85Cc7cSnFZ1+1rWS5T7Dex9hyLLGxP288pQQSS8TIfd9kMuY41QUZilgCSZ6DxGxzdqfp8POrXf3EU2nufGqAq85cgSdZJKQVjECSmOIFp3UBVF1mU6zFby7nrQ8yMmZ1spGQVmEOeFxWm18psREEQRAEQAQSodbYkzLlWjptfm1LfWKZCCRN3eDxw6pzHQe12u6RZ53SDsDhSkGtyShMjeQ7SHwNy+MYRayyo2IdSYqhtRFISoa0rje38+QgGY4kSGc03cHyAkmD31ObDJKp4gLJ9x46ygMHR/jotaezOljazdLW4GVsOZbYZB0kJZ5boEiJTXwc0BAIZkXFriICiSc5xdVnreTupwe486lBJuIprt3aPb2v3JBWMP9LSZM7c2Z3c1XtfpPpDMfGomUFknPXttJAbPqxBGGW2CU24iARBEEQBIMIJEJtsSdlDZ1Lp4uNfcV4OUz+J/qgudv8vvYi6H0MUpXdIL1WQOuzLYHkyEgFJ8h4r7nNyyCpN6UoS52IVYYUc+ggScVNNkWprIa6VhPGOU8Cif3aVHKQNPo9RGoikIyY2xyB5Hgoyj/fuodLNrXzxovWld08WO8jtBxLbJw6SHLfN3ZJYN20QJIf0mqVryQmuHbrSiYTaf7xV0/RHPBwyaaOHIFkZkirXa72wtO7eOToGGGHx/TY6BTpjC4rkGxb00KDK54/RkGYBR4psREEQRCEPEQgEWqLPSlrXLEEHSTLoHxkoh+aVprf114M6QT0PVFxMzug9aKNbbhdqnKr37DV4reloMQmHTediJYy2RIbhw4SO1ellECi1Ly2+u0Pm/+DchkkUEOBpKDERmvNDT/ZSUZr/qVMaY1Na/0J6iApFtJqlwRaDpKmgIeA152zjSU+xCM865R2Wuu99IVjXHXWSnwel9mXyzNTlMnJ83nhGV2kM5rf7s9vE1yKbIvfMgJJg9/DlqBr+rEEYZbYJTYS0ioIgiAIBhFIhNpiCyRNK5eQg8SaEC31DJJ0ypSPNK0yf6+5yNw6yCHpC8VwuxTdLQHWtNZxuKKD5Li5bS4IaQXT8WWpkknDpDXRdJpBYgtj5bIa5lEg6bMEkooOkkCtHSQhAH68o4ff7R/mI9ecztq2ypPptnofoWhy+U2YKjpIioS05jpIIgUtfiHHQRLB63Zx1ZlGvLx2qyVixieMM6VQdMoRSLavCdLW4HNcZmMLJKeUEUgATm0zX98ZEUiEOZB1kKSX2f+7IAiCIMwTIpAItSUWNvkj9R1LyEFiCSMJh46DxWJyyJSC2A6SphXQusGRQNIbjrKiyY/H7WJdW33lVr/hIgKJnWWwlJ02UyPmGIFzgcQW6spNJIPrTBvoKtuxOqE/HMXvcRGs95Zdz2SQpOf+gDklNv3hGP/wq6e4aGMbb3nWekebB+t9aA3j0WVWZpOq8DoXC2nNdZCMx/M72ORtYz5D3nHZRl5/4Vqes7nTun9iZkArWCU2Zjxul+L5p3Zy795BR6LToeFJWuq8tDb4yq63ucVav4qsYkEoxOVSKAWpjIS0CoIgCAKIQCLUmlgYAi3mav1Sc5As5Yk/mPwRMCGtNmsvNkGtFSbuvaEo3Vbw5ob2Bg4PV3KQ9BgRK7clavZq+RI+TnZAKzjPILEdMeUEktb1RkCLjs1+bCXoC8dYFayrWNrS6PcwUcMuNjoW4m9v2UUyneFfX7UNl6v849u0NhghZ9mV2dgOklJtfr0B0xmqVAZJMQeJ22scKZaosmVFE5991TZTXgOWQFIQ0ApW4PG0SPmC07sYm0ry+LHK76/DI+U72NisbTSfCY/1L7PXSVhyeF0uySARBEEQBAsRSITaYgsknsAScpBYE6KlXmJjCyTNuQLJRUYUqNCC1p6EA6xvr2c8liJUboIbPp6fPwLTJSjLRSBxmkFiT1QrldjAvJTZ9IdjrGwuX14D0Oh3E4nXwLVhdbGZGh/l7j2DfOiq08rmWRTSWm+cC0tdINnVE+aiz9zJ4Lj1OWM7SDxlsl78TWUzSGYIJGC1vy7x2ZGYKB6Saoe0WsLmc0/txO1S3PFU5TKbQ0POBJI2b5I0ioePLREhWli2uF1q+ZXUCYIgCMI8IQKJUFtsgcS2mM9DyULVLHcHCZRt95vJaCOQWBkX69vN5Kpsq9/x4/kdbGB5lNhEciaYjkts7JDWMpPOeRRI+sKxivkjAI1+L7FkhlR6jlb3SVNik54Kcd66IG+7dGNVm2cFksmlXWLz2LExBifi7Oyxco8qOUjAuD0KM0hcXqbwE4mnigsk/sbS4mq5Eht0ViRuqfPy3C0d/N/jx8tORKOJNL3hmCOBRCWnSKg6HusJVVxXEMrhcSmSc/3cEQRBEIQTBBFIhNoSC0MgaAUlatOFZbHJCiRLPINkoh+U27RItuk601yhLpNDMjKZIJHK5DlIoEKr32IOEltASC7hVr8TVovf+vbqu9g4cpCUd+pUSzqjGRiPsdKBQNLgN91T5pRDkopDYoIUHhr1FP/6qq24HZbW2CwXB4kdfntwyBIvZusgqQsyHDFi0IwMEjAtoEs5SEoKJPb/0rS749Xnr6UvHOP3B4ZLDu/IaOUONlkSk6S99ewfjDAeW9pilrC08bjFQSIIgiAINiKQCLUlFpp2kMDSyCGxJzdLvcRmvM+0R3bltBl1uWHNBWUFkr6wOca2S2Fdmy2QlBA64hMQD+cHtEKOg2QJH6fIoBGMmrqdZ5DYJTal2r+CEfX8zTV3kIxE4qQy2pFA0hTwABBJzCGHxApoPZLpxKU0m1uqn/QslwyS/kKBJBvGW+ZYB1pmZpAEggxFzL66ipVC+RtLi3FlHSTkiY1XnNlFsN7Ljx/pKTm8Q0POOtgAkJjE7W9Aa3jiWKjy+oJQArfLRVK62AiCIAgCIAKJUGtyM0hgaeSQLKcSG7uDTS5rL4aBJ0tO0npDZmJoO0gCXjfdLYHSrX7tDjYtpUpslrCDJDIAjV2WE6DakNYyk06lILjedLKpIf1WPoazDBIjTETmEtRqBbQe1tb7yG67XQWNfg8el2Jsamm7Emxh8BlLVJhu81u9g2RwPA6UcpCUySApKZDYeT7T/0t+j5uXbV/Fb57sJ1yiQ9ChkeocJL66ZpSCx46GKq8vCCXwuhVp6WIjCIIgCIAIJEKtyc0ggaXhILEnROk4pJfwpG+iLz9/xGbtRaa17fFHim7WGzITQ1sgAeMiOVrKQTJuXcEu6SBZwkJSZBAaV87MkiiHk5BWMGU24WNzG18BtsuhmhKbSHzuDpK5CCRKKYL1vvIhv0uAGQ6SVNS0GHeXaafsbzbuKZvomOUgsQSSohkkTcXFyUzaOESKCST2e62gXO3V568lkcrwiyd6iw7v0NAknU1+Gv2e0s/BJjmJO9DIlq5GHjta++5LwsmD26Wki40gCIIgWIhAItSOVMJMCLIZJCwRB8k4YOUwLOXykYm+/A42NqsvAFTJoNa+cBS/x0Vr/fTEcEN7Q+mQ1qyDZDmW2OQ6SJxmkNilFw4EktDRmgYLD1ThIMmW2NREIFlh/p6FQALQ1uBldHLpCiRam2DiOq+bsamkGWsyZtwj5dopF75vYiHT4ncijktBW4OvyDYlQlrt/ZQtsckXiM9e3czpK5u4uUSZzeGRSTa2O+w4lJgEbz3nrm3lsWMh9FIIxBaWJR6XIiUlNoIgCIIAiEAi1BL7in6eg2SRyzW0NhOZxi7z91LNIUnGzNXsYiU2dUHoOqNkDklvKMbqYB0qZ2K4rr2e4UicyWKT7fHjgJrpVvEEzP2L/ZqVIzJocloCzc4zSJKT4PbnZ7sUI7jOiENTo3Mfp0X/eAy3S9FerHSjgAbLNVD0NXOKVWIz7LPKp2YpkATrfUu6xGZsKkk8leGijW2A5SJJRcvnj8D0+8YWE6KhbIvf9kZ/8UDbUiGtZQUS20GS78ZSSvHq89fw+LEQBwZnCnyHhp21+AWMM8rXwHnrg4SmkhwaXsLOL2FJ43G7JKRVEARBECxEIBFqhz0Zy80gSS6ygyQZNeUpthiwVN0RxVr85rL2Ijj2MBSpE+8NR+kO5k8MN1hXoYsGtYaPGyGmsBRBKStvYYlOtJJRUx5RrYMkMVW5vAagdb25rWEnm/5wnK6mEhPvAuyyijllkEwNk8FFrGGt+TsamtVuWuu9S7rExs4fec7mDgCeGYpMO0jK4W8CnTbvpUzGfGZZDpKi+SNgHCSJyMz/PUcCycwSw5efuxqPS80Iax2PJRmOJNjYWYWDxNfAuetaAckhEWaPtPkVBEEQhGlEIBFqRyxkbnMdJKlFziCxJzFZgWSJTv7t9rUlBZKLjTgwvHfGot5QlFUt+RNDu9Xv0dEiz3e8Z2b+iI2vYekeo8iAuW1cYbIk0nHT1rYSyWj5gFabbKvf2nWyGRiPscJBeQ3kCCRzLLEZV034m9vN37MusfExOrl0HSR2/sh561vxe1wcHJp05iDxN5vb+LjleNPZDJKi+SNgREOY4QaZrUDS0ejn+ad18dNHj5PKmZQethwgGxyX2ETA18Dmzkaa/B4elRwSYZZIm19BEARBmGZeBRKl1DVKqb1KqQNKqRuKLP+CUupx62efUiqUs+zXSqmQUuqXBdtsVEo9aO3zh0qpIkXjwqKwFB0kWYFkZf7fS42KDpKLzW1BmU0ynWFwIk53MF8gWWcJJEVzSMLHZ+aP2Pjql7BAMmhubYEEnL2eyUlnDpIWy3VRQ4GkfzzmKH8Epkts5iSQTA4zRhNNwbkJJHZI64LkWjz5M9h3e1Wb9FkCyZrWOjZ2NHBw0KmDJOd9Ywu6loOkq5RAYgsghe+1hPW3r0xIa4n/pddcsIahiTi/2z+cvc8ukTnFqYMkaUpsXC7FOeuC4iARZo3b5ZKQVkEQBEGwmDeBRCnlBv4LeBFwJvAGpdSZuetorf9Ka32O1voc4D+Bn+Ys/jfgLUV2/S/AF7TWm4Ex4O3zMHxhNuQKJFkHyWILJFZORfMqc7vUS2yKhbQCtJ0C9e0zgloHxmNoDasKuqQ0B7y0N/g4UtjqV2uTQdJc0OLXZjk4SJqsDBJwJgAkpqbfj+WoC5r3bi0dJOGYow42AF63C7/HNacMEj05zGC6ka6WeiMGzFIgaa33ksrouYk1Tshk4FcfhN98tKrN+sMm26Wj0c+mzsbqMkjA5JBY5UcZf4spsakokBR8dswipNXmBad10dbg48ePTHdNOjQ8iVKmA1VFMmnz2Wo5o85dG2RP/zhTiXl+vYQTEo9LkZI2v4IgCIIAzK+D5CLggNb6Ga11AvgB8LIy678B+L79h9b6LiDvkp0yKZQvBG627voW8PIajlmYC/ZkrC6Y4yBZKiU2loNkqU7+J/rMMQsEiy9XyrhIChwkxVr82qxrr5+ZQRIdM1eeSzlIvA0zSwmWCnklNiWu6hcjOeWsxAasTja1ySCJxFNMxFOOS2zAdLKZmIMokZ4cYUQ3m8cMtMxBIDHGvNB8B7X2PWY674wcgNFDzjcLx1hhZbts6mzg2FiUTCI6/blTiuz7ZjzrIIm4GklldOUSm0TBe81RiU3xwGOfx8XLz1nNnU8NMmZ1Czo0PMmqljoC3gphwjD9OWZ1njp3XSsZDU8cm93rLZzcSBcbQRAEQZhmPgWS1cCxnL97rPtmoJRaD2wE7q6wz3YgpLW2ZxAl9yksAnYg5FJykNiOkSbLQbJkS2z6jYhTrkXp2ovMRHJyJHuXHVa5KjhzYrihvWGmQDJutfhd4hkk/eHYTPdLZBBQUN+RnyVRiaTDkFaA4PqaOUjsnIyVLZU72Ng0+D1z7mIzppvoaqqNQDLvrX733zn9+4E7S69XQF84mi0r29TVSDqjScQcOIVy3zfW59Vo2rw3SjtILIGkGgeJ2wfKXbYj1GsuWEMineHnT/QCJoPEeQcbWyAxYz9nbRCAx45JDolQPZJBIgiCIAjTLJWQ1tcDN2ut07XaoVLqXUqpHUqpHUNDQ7XarVCOWBhcHnP1VBwk1THeNy3ilMLOIemZLrM5HjLHt7uliIOkrZ7ecJR4KuffKmwJJC3lSmwWv83vR36yk5f/1+8ZmsgJYY0MQEMHuD3VOUgSU9NX9CthCyQ1yN4YGDcCSTUOkka/Z/ZdbDIZ3PExRmhiRbN/bgJJg+lwNDbfnWwO3Amrz4fWjXDgLseb9eeULp3SYQSMVNyJQJLzvrEcJINJs03JLjZZB0kVAolS5j1X5vPvjO5mzlrVzI8fOYbWmmdmJZCYsbU2+Dilo0FySIRZ4Xa5SIpAIgiCIAjA/Aokx4G1OX+vse4rxuvJKa8pwwgQVEp5Ku1Ta32j1voCrfUFnZ2dDocszIlY2EzKlFo6DhJ7EtPYBcq1tDNIbBGnFKvONQJUTplNXyhGS503G/CZy4aOerSGY6M5k7Rxq7XoEnaQaK157OgYY1NJPvHz3dMLIoOmvAbysyQqkZysQiBZZ676T41UXrcCWQdJFQJJg98z+9yPWAilM4zWoMQmuBAlNlOjcHwHbL7C/By6z1FXIq01feEY3dZxtUNNM8lo5ZDWIhkk/UkjjMwqg8RbD64SJTHeurIOEoDXnL+G3cfHeeDgCBOxFBucCiTJ/BIbwApqHVuYYF3hhMLrUqQlg0QQBEEQgPkVSB4GtlhdZ3wYEeTnhSsppU4HWoE/VNqhNmd+9wCvtu56K/B/NRuxMDdsgQTA7TWT+UV3kFgTaH+zudpaOMlZCmhtldiUCGi18dZB9/a8oNa+cJTuEiGg6612oXmtfsPHzevS2FX8MXwNiy4iHR2dYjyW4vSVTdy6q59bd1kBthP90+OupotNopoSG7vV79xzSPrH7RKbKjJI5iKQTJqOKKO6yUz25yCQtC1Eic0z94DOTAskyUk4WvFrgPFoimgynT2uDX6P+R9IxiqHtPoKHCQuD/1TRuCoLJAUiHHxieLukexj1Vd0Y113zmq8bsW//ca07z6lWgdJjvB33rpWhiMJesYW+TNXWHa4JYNEEARBELLMm0Bi5YS8F/gN8DTwI631k0qpTyulrstZ9fXAD3TBZS+l1O+AHwOXK6V6lFJXW4s+AnxQKXUAk0ny9fl6DkKV5AokYK7mLgUHicsLHr8RSJaigyQ+biaHpTrY5LL2WXD8kWz75OOhGKuLBLQCrLe6YRwezpmkjR83pTwlr3rXV7zqPd/s7DGT+n951TbOXt3M3/9st5mo5zpIspNWBwJAMlqdgwRgbO4CycB4jKaAh3rfTHdPKeaUQTJlBJJ0oA2v22UJJKFZ7aq5zotSEJrPEpv9d5pQ4tXnw8bLTG7H/jsqbtY3PrOsbFNnI+6Mgza/bo8J7LUzSAJBhiIJ6rxuGou4sIDyJTblBBIH/0ttDT6uOGMFjx8LATh3kNjCiz024Nx1QQAePSo5JEJ1eN3S5lcQBEEQbOY1g0RrfavW+lSt9Sat9Wes+z6utf55zjqf1FrfUGTby7TWnVrrOq31Gq31b6z7n9FaX6S13qy1fo3WurInW1gYCgUSb2AJOEgiZhKj1JJwRxRlot/cVnKQAGx6gRGdjvwesMMqi181b2vw0eT35Iedho+X7mADZsKVipk2oovEruNhfB4XZ65q5t9evZ3xWJJP/t9uk0FiCyQeP7j9lR0kWlshrVV0sYGaBLX2h2NVldcANAbm7iBRDVZJYaDFCAGzeC3dLkVLnZex+SqxyWRM/simFxqxztcA6y9xlEPSF57pzDmlswFfJo6u1MUGzOeB3cWmLsig1eJXlQpI9taZ8rxiJTZlBZI6R59/rz7f5AF5XIo1rQ7aUcP051iOM+q0FU3U+9ySQyJUjdslIa2CIAiCYLNUQlqFE4Gl6iCxJzH+xkXP1yjKhFVCUimDBGD9pUYYOHAXU4kUoalk0Ra/AEop0+p3NNdB0lM6fwSmhYRFPE47e0Kc2d2M1+3ijO5m3vuCLfx2537IJKcFEjB5EpUySJJRQFcO78zdZ11rTQSSgfFYVeU1YIW0ztpBYnJTvM22QBI0t046/RShrd7H6Hw5SAZ2weQgbLly+r7NV8LQ0xDuKbtpX7a19fSx3dwewKvSTGa8lR/bft/YDhJLICmJUqY0Zx4cJADPO7WTjkY/a9vqjfPHCYmZGSQet4tta1p4TBwkQpV4XIpkWjJIBEEQBAFEIBFqyZJ0kORMYpZqBsm4LZA4cJD46mHDc+DAHfTaE8UiHWxs8lr9ZjIw3lvBQWJdkV4kgSST0ew+Ps62NdPvoz9/wSae1WVEg0lf+/TK/qbKDhJ7gup16CAB4yKphYNkfBYOEr+HWDJDajaTFavEpj5o5bTY/4uzDmr1zl+Jjd3Sd9Pl0/dtviJ/WQn6w1FcKr/rzJY2Ux4zHC/TJtvGft/EQlDXagSSUh1sstsU+exIRKYzTYrhUCDxuF388yu38qGrTqs8dpvkzBIbgHPXtfJk7zix5OI5wITlh7T5FQRBEIRpRCARascMB0lgCThIxvMFkoSDUM+FphoHCZiJ5PA+Ro/vBygZ0gqwrr2enrEpM+GeGoZ0AppLtPiFnLyFxRFIDo1MEomn2Lp6+n3kdbu44TmtAHxzZ86E099c2R2RnUg6zCABSyCZWwZJKp1haCJetYPE7kY0Ga9+gpuJDDOh62gPWgG2cxRIWut9jE3OU4nN/jth5TZoynEEdZ4GLWsr5pD0hWN0NQXw5LgtTmk1vw9GHXyl2e+baAjqggxFKjhIoPhnR+5nSzG8dY5bZl955gpevM2BQGpju1kKsnXOXRskldHsPj6711w4OXG7XCQlpFUQBEEQABFIhFqRikMqOm3rB8c1+PPKcimx8bc4z8mwyhJcB01eQ6kSG4AN7fUk06YtarZ0oZyDxJ5wJRfnOO2yAlq3rQnm3b8xYCaEP92X5O49A+ZOJw4Se4LqtMQGILjeOEjm0C51OJIgozHtdqugyRJIJuLVCxPx8SFGdRMrmq3J/lwFkgYfY/PhIImFTatq2zFioxRsvhye+S2kSz///iKlS10B81r1O9EjchwkaX8LoalkZYGk2HutYhebhvn7/EtMAWrG+/rcdUZIlBwSoRq8bmnzKwiCIAg2IpAItcGehC05B0luiU3D0iyxmehz1sHGpn0zBNcR7L0Ppcq3kbVb/R4ZmTIdbGBJZ5Ds7AlT53WzqbNALIoYUaS5czV/+9PdjMeSVpeWSg4Sux1qNSU26837dnKoipHnk23xW6VAMhcHSWpiiDGaWNFkPeacHSTe+RFInrkXdDo/f8Rm85XGqXHswZKb94VjM1xTKmWyuo87+fcONJtjEg0x5TKfDV0VBZKCEhutHYa0zlNHqMSk+V8tCJbtbPKztq2Ox45JDongHLdLSRcbQRAEQbAQgUSoDVmBJDh933xOEJySiBSU2CxFB0m/8/IasK60X8na0EOsanSVDXZc3261+h2ZNB1sAFrKldjYAsnivG67joc4a1VzXvkEYAQST4BPvvoSBidifOaXT1fnIKm2xAbmlEPSX6TTihMaA0YgiczCQaInhxnRzdOulTlnkPiIJTNEEzXOszhwpylzWXPhzGUbnwsuT9kckv5wkfDblHFqHB13cBXc32wJbppxzPvdWYlNjkCSikEmVZOQ1lmRiJR0nJ27tpVHj4Tm53GFExKPS5GSEhtBEARBAEQgEWpFKQdJcgk4SOxcDV+jcRQsNSvxeJ+zgNZcNl+BPxPlBfWHyq62oimA3+MyrX7He8xrUt9eeoOsQLLwTpu0FdB6dk7+SJbIIDR2sX1dK2+9ZAM/3HGMuLsR4hUm/3aJQ7UhrTCnHJIBy0FSbYlNo98NQGQWDhJ3bKSmJTZtDT6A2rpItDb5I6c8H9xFOs4EmmHds806RRiPJYnEUzNzd6zPmd5JKgs6/mbQ5jMgpB0KJP6mfAeJ/XslgSQVm5/PmzKtq89dF6R/PEZfeJHLG4Vlg8ftkpBWQRAEQbAQgUSoDbGQuc3rYrPIbX7TKTOR8FuhlX47gHQJldlkMhDpr14g2fhcknh4nuvxsqu5XIp1bfWmxCZ8HJpXzbDl57GIJTYHhyJEk+m8DjZZIgPZFr+XbekAYCTlMwJYuayQbIlNNRkklkAyNnuBpH88htetaLdEBqc0+o1oEIlV2epXa3yJEGM00W53ZPE3A8qEkc6C1nozlpoKJINPw0TvzPyRXDZfbtoA292dcrCdOd2FnZssB0lM+zg0XOG9myNqjKTNfqoOabXDgSuV2OSMraYkJkuKfudJDolQJR6XIrXULhwIgiAIwiIhAolQG0o6SBbxKqY9ocktsYGlVWYzNWKs+lUKJNrXwA59GttiOyquu95u9Tt+vHz+CExPuhyWBgxOxPi33+xhOBJ3tH45dmYDWks5SIxAsqXLvJ4DCb9xApQb62xKbPyNxmUzhxKbAavTisvloO1sDg2Wg2QyXqVAkpjEm4kT97fhth/T5TIiyRxKbABCUzXsZHPA6lBTViCxllkhxLn0ZQWS4g6SGF4ODlUQQAPN2V8Hk0bEaG9wmEFii3Hxgs+WYsxnuZqdQVKEM7qb8XlcPHpEckgEZ3hcLjLatFkXBEEQhJMdEUiE2lBMIFlsB0nhJMa3BB0kE73mtpqQVsyk9Z7UNlZED05ni5RgfXs9R0Yn0eHj5fNHoOoSm58/3st/3XOQF3/xdzx0aNTRNqXY1ROiwedmY0fjzIWRAWjsAmB1sI46r5vjUyavo2xQqy2eVFNiA1ar3zlkkIzHpktdqqDJcpBMVCuQTA0DkKkrKJ+qa5lzic3oZA0dJAfuhK4zy3dSWnE2NK4smkPSb5WNlMogiStfZYEkR9ToS/hprffi81T4KvQ3QSZpunWBM4HEdpDMRw5JYrKk6OfzuNi6uoXHjoVq/7jCCYnHbURVCWoVBEEQBBFIhFqxFB0khTkBtS6xmRqFPbfObR8T/ea2SgdJbzjKvZlzzB9FrrTnsqG9nkQyZXXLqeQgqQOU46vefeEYfo+LOq+bN3ztj3z1twdnfRVy5/EwZ61umXZA2KQSxmljOUhcLsXmrkaOTBq3Rdmg1uQsHCRQE4Gk2oBWmHaQVF1iMzUCgGroyL8/MHuBJGiV2IRqVWITn4AjfzAlNOVQyrhIDt5tyuRy6AvHUAq6moo7SILNzTwzVKnEZtpBcjzmr1xeA+CzPkPsz46qBJJ5KrEp0xb8vHVBdh0Pk0hJ2YRQGfszV8psBEEQBEEEEqFWxMLg8uZnPXjrTDvPdA0t+tWQncTYIa3WhKJWrX4fuhF+8IbKrWbLMWHlLFTTxQboDcXYp9eQqF8J++8ou+769gaudO1A6TR0nVF+x0qZ4+SwDKk3FGVNax2/eN9zuOaslXz2tj2889s7qp5UJ9MZnuodZ1uxgFa73a4lkABs6WrkQNj6+IqXOf620OOpIoMEjEASPlY+36QMA+FY1QGtYMISA14Xk4kqBZJJI5D4mgoFkuDsBZI6O6S1Rv+/h35nXBibi7T3LWTLFWbcxx/Ju7s/HKOj0T/T8WE5SFa2tzpwkEwLJEcnfTPFlqLbWJ8h9meKfesrJ5DY5WrzUNKXnJx2xBXh3HWtJFIZnuqbw2eTcNLgcYmDRBAEQRBsRCARakMsbK5W5waAeqyJx2K5SLICiTUhqnWJzeDT5jY6h9KS8T5A5U3+nWA6VCjSmy6HZ+4tK0KtD7r5W8/3CDdugjNfXnnnvgbHx6g3HGNVsI6mgJcvvfFcPnXdWdy3f4gXf/F+HjvqPANh/0CEeCrD1lIBrZAvkKxo4uikVWJTTiBJThpxxFXlR11wvSkPsx+7CiZiSSYTaVbOQiABaPR7mKjSQZKcGASgLlggtM3BQeLzuGjye2oX0nrgDiMarHtW5XVPeT4o14wym95wbGb+CGQdJKs723hmaLK8i8nOIHF5OBpRDh0kBZ8dhflGxZhvB4m3tCvq3HVBgKr+B4WTF1sgSUurX0EQBEEQgUSoEdEQ1AXz78t2cVikHJLCThP2ba1CWof2mttZdgkBjIOkobN4y9MyHA9F8bld+E+/yjzPnodLrrtm33dY7xrk9jXvA7en8s699Y5zE/pC0eyEVSnFWy/ZwM3vvgSA1/73H/jG/YfQDlwYu46HANi2JjhzYcRM/gsdJBGs91fZDJJo9eU1YAQSmFWZjd3idzYlNmAEkmpDWifHjJDT2FYgtM1BIAEINngZq0UGidZG7DjleeBxIEjUtcKai6ZDXS36w9HiAonlIFnT1UY0maZvvMxnjvU5oANBhiIJZwJJ1kFSTYmN9b6bF4GkdJtfMF1+ulsC0slGcITbbU4Fk1JiIwiCIAgikAg1wnaQ5DKfV1CdMCOktSH//rmQTsHIAfO73eJ4Nkz0V11eA9AXMhkXrk0vAOUuGmgJwOQw7t99jgfd5/HbzHZnO/c1OhKREqkMQ5E4q4L55Svb1wb51V8+h+ed2smnf/kUtzxWPkQWTAebpoCH9W1FxIysg6Qre9eWFY1MaGvdcq9nYqr6gFaYbvU7C4GkP2yCPGdTYgPQ4PcQqVIgiYUHSWg3bW0FIa1zFEha6321KbEZ3m+OZaX8kVw2XwG9j0FkKHtXXzg2s8UvWA4SxcYVbQAcHCzjgLIcZZlAC/FUhs7GWWaQKHf59tG2MFfrrlmZtBGEyggkYFwkj4qDRHCA13aQSImNIAiCIIhAItSIYgKJXWKzWA6SREFIay3b/I4dMnkKMEcHSS80r6p6s75wlFXBgDnmay8uLZDc+8+QmOSWrj83rX6d4Kt3dIwGxmNoDauKTFiD9T5ufMsFnLaiiRvve6aii2TX8TBbV7cUb4ubdZBMCyRrWutJeGzBq0KJTblJbCmCa81t6EjVm/bbDpI5lNhUK5AkJ4YYpZkVzQXPNdBiykHSVWaaWLTW+2oT0mq/P8u19y1ki93u924AIvEUE7FUcWdOKgqeAJusFtDPlMsh8TWAcpH0GqHEmYPE+gyx32vxCXOfKtPGeb4EYvt/s5JAsraVnrEogxOL2ElMWBZkQ1qlxEYQBEEQRCARasRSdpD4CkJaa5FBYpfXwKwcJA8cGDalC7N0kPSGYtPCxJYroO8JmCjIyxjcAztuggvehm/lGRwemXRU7uI0pPV4yLyuhQ4SG5dL8afP2cCe/gn+cHCk5H7iqTRP940Xzx8B4yAJBPNKM9wuRXenFUhayUEymxIbXwPUdyxKiU1TwFN1FxsdGWZUN89sLWz/T5YTkcrQWu9ltCYCyR3QvgVaNzjfZuV28xpY4kp/2BzXkhkk3gAdjT6aAx4OlutkoxT4m4i5qxFIipTYlCuvgZwSmxq3+c12ZiovkJy3PgjA41JmI1TAa5XYSEirIAiCIIhAItSKpeggiU+Y8gqX1Q7W5TaTllqU2Aztmf69SgdJMp3hT77xEJ/79W7ToaXKFr/pjKZ/PEZ30Dq+m/OvtGe5/e+MOPT8v2VdWz0TsRQhJ+USvgZHkzoTFMv0OIrwsnNW09bg4xu/P1RynX39EZJpzbbVweIrRIqLSJtWtDBFoHIGyWxKbMCU2YzNwkESjtFS5yXgdc/qYRv8nqq72Lhjo4zRRGu9L3+B/T85yzKwYL2P0OQcS2wmBuDw72GLg+41ubhcsOmFJoSYaYGkqDMnFQVPHUopTulsrNzJpr6DCU8r4FAgKQxpXUyBxBYvK7yvz1rVgtet2N0rnWyE8rizJTaSQSIIgiAIIpAItWFJOkjGZ05iHOZrVGR4HzSvBpen6slnOJokldE89qQlslQpkAxNxEln9LRzY+U2E2CaG2i5/05z5f15H4aGdja0m8nUkVEHkzWvsy42vSEzYS1WYmMT8Lp588XruGvPIIeGix/3ndmA1lIOksG88hqbLSuaCOt6ktEyGRvJydk5SABa188ug2Q8NuvyGrBKbKp0kPgSo0x5gjNLlAJBczvLHJLWeh8T8RTJ9BwmTnf/A+gMXPiO6rftOBUmByEVp9cW5IpmkETBa475JicCyWu/xQPr3g1AVzUCyawcJItTYhPwurnvb17AX12xpbaPL5xw2F1sklJiIwiCIAgikAg1IBmDdHxpOkhmCCTOW9iWZWgvdJ5mJqBVOkhsF4c/amVrVCmQZEtb7ImiUrDpcuMgyaRN3sTtfwetG+GidwGwvt1M1g6XECnycFhi0xuK0lrvpc5X3inx5mevx+tycVMJF8munjDBei9rWksILZGBom2QN3c1EtF1TIbLtFlOTM0ugwSMgyR8DKq8qtofjrFiluU1MLsMkvpkiLi/beaCrINkdgJJW4PprjTrVr/9u9CP/S8Pdb6K3bGO6re3hbHIQNZBsqKliKCRjJl2zsCmrgYGxuPlj+HKrRxKtuJ1K1rqHHSQcnvM/u32vk4EErcH3L7ah7RmBZLKwl93i3HVCEI5PFaJjYS0CoIgCIIIJEItsCdfS85BEpk5ifE3Tl8Fni2ZjHGQdJxmWhtX6SCxQy9XKKvDRJUZJHZpS172x5YrIDoGxx+FR24yJUBX/UM2t2Ndez1ul6p8ZR2skFYnJTYlOooU0NUU4KXbV/HjHT2Ei5T47OwxAa1FJ3JaWw6SmQLJqSuamKCO2GSo9IMnZ9nFBoxAkk5Md9FxiHGQOHAllKDR7yGeyjh3baQSNOhJMoH2mcvmKJAErZIdR6VZhWgNv/lbYt4W3nHkhfxiZ2/1+7D/NyKD9IVjdDT68HuKCHKpfAcJVAhqxTixOhv9zgWE3M+O+MS0q6Qc3rp5dJA4eHxBcIDtIJEMEkEQBEEQgUSoBVmBJJh//5J0kDTO3UEy3mMm3nN0kJzRaMaRbqzOQdIbKpL9ccoLQLlg90/gnn+C9c+B01+SXez3uNnY0cCefgf5K75GM+HMpCuOo1RAayF/+pwNRJNpfvBwfslKLJlm38BE6fKaRMQc6yIlNmtb65iknvRUuRKbWYa0AgTXm9sqOtkk0xmGI/E5ldg0+D0ATDp1kUSNg8bVUHuBxM40GZuchYNk721w6D7+LfZyxmnMOkCqIs9BEi0dfJvrIOk0glglMXAoEneWP2Ljb5rOL3LiIAFTZlPzkFZnJTaC4BSP2+5iIxkkgiAIgiACiTB3KjpIajxBcMp8CSR2B5vO06CutXoHSdQIJJetTJPQbh6qzqBAbyhGo99DcyCnNKC+DVafDw9+xThJrvmnGS1IT1vZxF5HAok18arwuhmBxJkQcNaqFp51ShvfeuBw3kn4033jpDKarSUDWu0WvzMdJB63C+1vQiUqdLGZdYmNLZA4zyEZmoijNXMrsQkYgWTCYQ5JLGyOkae5c+bCuQoksy2xSSXQt3+MHvda/s99NVu6GukLzUYgsV73yAB94RgrC9sYZx9v2kGyrq3BuKUGy5e2DE1UKZDkfnYkIuBvrrzNfAgk2ZDWWQp/Qs1QSn1DKTWolNpdYvnpSqk/KKXiSqkPFSy7Rim1Vyl1QCl1w8KMuDhucZAIgiAIQhYRSIS5YwsEpRwkySXkIPHXIKTVFkjsEpuqHSRmsnlGQ4QhWrl1d3UKSV+4hDCx2eoScs6boHv7jMWnr2ji6OhUZWeCPfEqc5wi8RTjsZSjEhubtz/nFHrDMX79ZH/2vl3HzcS9dECrdWyKOEgAPPUteFMlBK9Mxpo4z7bEZq25rcJB0m+3+J1jSCvguJNNeNiUrgRaZopI+BqNs2iuDpJqS2we/h/U6EE+Fn09H3vZNs5c1Uzf+CxKTRos0ScyaDo3lXWQmGU+j4v1bfU8M+ygxKZqB0nEOKsSRcr3iuGtlxKbE5tvAteUWT4K/CXwudw7lVJu4L+AFwFnAm9QSp05T2OsiMdltfmVkFZBEARBEIFEqAGVHCSppdTFpmHuGSTDe6G+AxrajShUdQZJEpeCQGyQWF0Xv36yv6pwvN5QieyP7a+DU18El3+86HanrTTHYt9ABRdJtqVpaYGkzw6KdeggAbj89C7Wt9fz9funw1p39oTpaPSVnvhOWGJKY/GclrrGVuoyU0wVExPsK/ezLbHx1kFDV1UOkgG7Fe0cQ1oBx51sJkaNg6SprYiI5HIZp8OcBZIqHCRTo6Tv/Sz36224t1zFy89ZzcqWAAPhOJlqr1C7vVDfTircT2gqWfq4pqJ5TqFTOhvLOkjSGc3opMkgcYyv0YS02i4SRwJJ3fw5SGb7vhZqhtb6PowIUmr5oNb6YaBQYbwIOKC1fkZrnQB+ALxs/kZanmyJjbT5FQRBEAQRSIQakHWQFAgkbh+gFsdBonXxq7y+ptqU2HSeZn6vC5rJZxUnlqFogmC9DzXRT13bGoYm4uw4XKYTSwHGQVJEIGndAG/8ATQVcRIAp680JQEVc0h8lR0kvZYQ4DSDBMDlUrztkg08djTEo0dNQO2ucgGtULbEBqCxpZVGFeNg//jMhfaV+7mUIgTXVSWQ1MJBYmeQOO1kEw0Zl02wY1XxFQItsxZI6nxuAl5XVRkk+t5/RsUn+H/qT/jMK7ehlKK7OUAinWF0Nt1wGlcQD/UBZQS5HAcJmE42h4YnSwqPI5NxMho6q3md7JBWO4fE78DB4TDwuCqSU4DKZq4Iy5LVwLGcv3us+xYFO6RVutgIgiAIgggkQi0o5SBRylxBXYyQ1lQMMqnSbX71LE8Etc4XSAJB0Jnp9p8OCE0lCdZ5YaKPzlUb8Htc3La7v/KGmFDT4UiCVbNwKKxpraPe566cQ2JnkDhwkJR0fpTgNRespSng4Rv3H2IqkWL/4ARb1wRLbxAZAJfHZL0UIdhqWsce7ity/JI1yGoIroOx6kpsfG4XbQ2+WT9kU6A6gSQ5MUhGKzo6S3RDCrRUXQaWS2u9z3mJzdA+9MNf5/upF/CGl1yTdXystBxPswtqXUHGchKVzyCZfp03dTSSSGfoGSsuTgyOxwGqc5DYIa1ZgWQRS2x8DcYdJJzwKKXepZTaoZTaMTQ0NC+PYWeQJKXERhAEQRBEIBFqQCwMbn82JDEPT2Bx2vzak5jCOn1/oxE0csb0+wPDHBt1eJU3MmgcMx05DhKoagIamkqyIpCC+Dje4Cqef1ont+3uc1R+0D8L54aNy6U4dUUTe4q5LXKxj1myjIMkFMWlYEWVTokGv4c3XLSO23b3c8dTA2Q0bFtdIn8EzPFu6Co5GWxtM51bevqL5Lgk5lhiA9C6HsI9FTv62AyEY3Q1V9E6tgjVdrHRkyOEaaC5ocRrYbucZkmw3pfNzalE9FcfZTLj44H1f8Zrzl+Tvd92fvTNUiBxTRonUdkMEm++gwRg30Bxt9hQxBJIZhPSapfoOQppnY8Sm4gEtC5/jgNrc/5eY903A631jVrrC7TWF3R2FglirgFet/l8FQeJIAiCIIhAItSCWHime8RmsRwk2au8BZOYgnyNZDrD27/1MF+6+4Cz/Q7bHWxONbd2MG10zPHQQtEE632WSNHUzbVbuxkYj2fLTsrRGy7S4rcKTrc62ehyDhoHIa294RhdTYHsiXU1/Mmz16O15h9++TQAW0sFtIJxkJQIaAXw1Jlt+wcHZy60J6azDWkF4yDJJKezUCrQPx6bU3kNTGeQOO1i44qOMO4qU6Y0hxIbgLYGryMHiT5wN3WH7+RGXsnfvea5eeOxnSR94VmIpY1d+GPDgC6eQZLJQDqeV3Jy6oomWuq8/M3NT3Dv3pnvjaEJI5B0VRvSmpyaLilcrDa/iSlp8bv8eRjYopTaqJTyAa8Hfr5Yg5nuYiMZJIIgCIIgAsmJQDIGA08t3uOXE0gWzUFiCRDF2vxCtiRm/0CEWDJDT8jhJCbb4vd0c2s7SKoIag1NJVnrsSasTd288PQufB4Xv9rVV3HbXqtV6qoqusfkctrKJsamktkJYlEclNhU0+K3kDWt9bzo7G6GI3FWNPvLu1AiAyXzR4Ds6zsyMjxz2VxDWsEIJOA4h2RgPF5di9+hffD0L/J+Gp+5jatdD7Py+J1meQV88VGmPMHSK8xRIAnW+ypnkGTShH/2YY5mOll99QdnOJw6Gvx4XGrWDhKPTrC2LknA65653BZgcxwkTQEvP/3zS1jRHOBt33yYz/1mb157afv931FtSCvAhPV/umgCyaQIJEsEpdT3gT8ApymlepRSb1dKvVsp9W5r+UqlVA/wQeBj1jrNWusU8F7gN8DTwI+01k8u1vPwShcbQRAEQcjiWewBCDXg0W/D7X8Hf/OMs5P2WlPWQVK/SA6SEp0m7GBFa/mu4yEAjo85FHGG9pqg16Zu83fWQRJyPLTwVJKVbmv9pm6aAl6eu6WTX+/u5+9ffCYuV+nyjENW69LZdkmxO9ns6Z+gq5Qw4SSDJBzjzFUOSgxK8KfP2civdvWxdXWw/IqRAVh1TunlfvO+m5wYI5ZM50+g7RIbq7tJJqP51C+e5KXbV3HBhjZnAw2uN7eho7D+2WVX1VrTH47xwtNLO17y2HUz/Ow9kM4XH9zAf/uAPcDxbvjg0ybPpwT1qRDh+vWlHycQnJNA0lrvrdjFZuype2iNHOB/2z/CXzx704zlLpdiRXNg1hkkAKc3lfgftT9fCkJLN3U2csufX8onfr6bL91zgB1HRvni68+lqznA0EScJr+HOl8RwaUU9mfHuCWQOGmz662rvUCcFIFkqaC1fkOF5f2Y8pliy24Fbp2PcVWL2y0hrYIgCIJgIw6SE4HQETPJihQpM1gIygoki5xBUiykFbKT/509ZuLYG4o5a0E6bAW02hPWKh0kyXSGiXiKLm2V0zSZYM0Xb1tJXzjGY8dK7+ePz4zwtfsOcdmWjuJX0h1gd7IpG9RaQSDRWhsHyRxa2Z63Lsi7nnsKb3rWutIrZdIwOeTIQdKkoxwcKsibyIa0mufz0OFRvvWHI3z1twedD7TFigkIVQ5qHY+liCbTlUtstIbf/wf85O2w5kJ4173w7vvzft7g/n/c2fU241YYO1x2d03pMOlAGcEn0GKORdph0GoBbfU+wtFk2cnT3qd3AvCiF72sZKlPd0tg1iU2AFvqSgh22W5FM497nc/Nv756O//26m08fizEtV+8nwcODjMUiVeXPwI5DpJec+tEjPY1mM/mtLNyKUeIg0SoMXYXm6SU2AiCIAiCCCQnBBEroHJqZHEev1KJzaJmkBRp8wvZVr+7jhuBJJHOMBwpU3Zik9vBBqp2kISjZpLanhkxE66AESwuP2MFPreL20qU2ewbmOBd397BuvZ6vvSG8xw9VjHaGnx0NvnLt/r11GHaMxcvDRidTBBPZWYVFGujlOJvrz2DF5xWxm0xNWICdcsJJNbxa1RR9hcGctoTZ6vE5uZHegC4b98w4zGHYoE3AI0rHQkkA1aL37IlNpk03PY3cMfH4axXwltugVXnwsqteT/9dZt5uO45ZptjD5XcXSSWIMgEqqG99GPa/5uxCuG8JQjW+8hoGI+WPmZjvQdI4eKUTaeWXGdly9wcJOv9Jd6zJRwkubzmgrX83188h+Y6D2/+nwf53b6h6gUSO89ovJoSG2tMtSyzSUxKSKtQU6TNryAIgiBMIwLJiYAtkEwWyWFYCKKhaSdFIfNhMXdCqQwS2yafiBBPpXm6b5wtXea+46EK44yOmWPdkTMJ9DWYNrQOHSQhK+yyJT2cdY8ANAe8XLalg9t2988IUB0Yj3H9Nx7C73XzzbddSEu919FjleL0lU3sHSgzWXa5zASshIPEzpHonmUOimPs93WZkFb79W1RU+wfLJhAJ6ZDWifjKW7d1ceZ3c0k0hnufroKt1VwnaMMEnvyX9JBkozCj/4EHroRLnkfvOrr4Ck+SW/wuznAGiPoHXuw5GMODQ3gURm8TWWOUVYgCVV8DsVobTDvt1JlNpmMJjN2lHFvF8pd+r25KlhHXzhWPiC4CLGA6dyxylPiPVvGQZLLaSub+MV7n8NLt69iPJaqugNT9rNjoteIMWWeaxZbyKjlZ2Bi0ll5jyA4xCMZJIIgCIKQRQSSE4GJRXSQaL3MHCSWNT0eYV9/hGRa86KzjVBRUSCxAzPtgFYwpTaBoGMHid0utTExPJ1jYvGird0cD0V5omc6L2IiluT6mx4mHE1y0/UXsqZ17leOT1vRxP6BSPmrhb6GrMumkF7rOK2eg4PEEVmBpIyDxFsPys2a+nQRB4ldYlPHrbv6mEqk+dTLzmJFs99RIG4WpwLJeBmBZHIEvnUd7PkVXPMvcNU/lmxdDKaTzURcw5oLyjpIxoZNdx1/S5n2n1mBZHY5JK31PvNYJTrZPNU3zorMAGm7HKkEK5sDxFMZRx1xchlI+IlrD12qxPgdOEhsGvwe/v115/D1t17AB67YUtU4sqLEeJ/zrKesQFI6z6dqEpNzCx4WhAI8buliIwiCIAg2IpCcCGRLbBbBQZKMmjao5dr8LoaDJBExzg5PwWTVN+0g2WkFtF5zthEqKga1Frb4takLVu0gCcQGZggkV56xAq9bcas1eU+mM/z5dx9l38AEX37z+Zy9ukw73Co4bWUT8VSGwyNlJm2++mkHRgG2QDLbVsOOsTN1yjlIlAJ/E6vqEhwYLBBIElOAAm8dP36kh1M6GrhgfSsvOrub3+4bIhJ3mAsRXAfhHlMeU4YBy0HS1VzgChk9BF+/Evp3wmu/Dc96d8WHbPR7zPjWXgyDT5Ysj4mMGIGksW1l0eXA3B0ktkBSopPN/QeGWaOGaFwxM5w1l+5ZtvrtG48zRJBWO7enkGw7Z2fvR6UUl5+xglM6q3Rh2A6SqeHp3yuRLbGp4WdgUtr8CrVlus2vOEgEQRAEQQSS5U4qPj3xWYwSG/uqdK0cJL94P/zu/819XPEJc5W3MDAyRyDZ1RMmWO/ljO4mmgKe7MS/JEN7zfMJFnQMqcZBEk0CGt/UIDTlOyNa6r1curmDW3f1kcloPvrTXfxu/zD//MqtPO/UMg6BKjmj20lQa2PZEhufx0V7g69mYyqKEwcJQKCZLl+SwyOTxJI5IkZyCrz1HBmd4qFDo7zq/DUopbh2azeJVIa7nh5wNo7gOsikYLy37Gr94zFa6735AbrJGNz0IoiOwp/8H5x5naOHnBZILjI5LMcfKbpeNGSeQ7Cju+hyYDonZ84OkuICyYP7e1mpxqjr3Fh2P3bnpWpzSPrCUYZ1C43J0eIrJJ07SOaEL8c14tRBkg08rlEGSSZtCSRSYiPUDjuDREpsBEEQBEEEkuVPbueaxSixqSSQeOumJzBOOHAXPPx1U7ozF+IT+RMaG48P3D6IR9jZE2br6haUUqwO1jkosdkL7VvAVdBBpioHSYImoqh03IR/FnDt1m56xqK87/uPcfMjPbz/8i289oLypQvVsrmrEZeCPX1lcki89SXLAo5bHWxKdSupGRMD5jWsdLXc30y7J0ZGw6HhnDEnp8BXz08e6cGl4JXnrQbg/PWtdDb5uW1Xv7NxtOa0+i3DwHhsZq7F5KDpRHP5x2Hds5w9HqYUJBJPmRIbVMkym8T4EAD1LU4ySGYpkFgZJKEipTGxZJreI/vNH8EyHYmYzqzpq1ogiTGkgwTiJQTglLMMkjmT6xrxO2xxXeuQ1qxbRkpshNohDhJBEARBmEYEkuXOUhdIPIHpCYyj/Y3D+HEY2jO3cdkOkmL4GknFIuwbmGDbGjPuNa119DgpsSksr4EqM0iSdLmsY9Yw0xVy1Zkr8LgUv9rVx2vOX1N9ToIDAl43Gzoayney8TWUdZDMe0ArGAdJufIaG38zTcq8dvtzy2wSU2hvHT959DjP2dKZHbPbpXjR2Su5Z+8gk07KbILOBJL+8VjWJZHFLq1wOqG2aAxYAkmgBbrOLBnUmrHL6ho6Su9sjgJJo9+Dx6UYLeIgefToGJ1p6zOo0FlVQGeTH7dLVe0g6Q/HCLtbcU2WCNZdKAeJJ2DK9qCKDJIal9jYThQpsRFqiFIKj0uRlgwSQRAEQRCBZNljlyHUty9yiU2w+HJvHaQTFfMbAOMasbvP7L9jbuOKj5cVSMLhUVIZzdbVQYDKDpLEpJkg5wa02lTjIIkmWOe3hIfGmQJJsN7Hay9cy0u3r+KfXrl13lwappPNLAWSUHT+80fAiH+VymsA/E3UZSZxKTiQ+5ySk0xpP8dDUV5z/pq8TV50djfxVIZ79jroZtNibVtJIAnHZwa02sewyglto89DIpUhkcqYMpueh6HI5MU9NUJMBaYn4sXwNYByz1ogUUoRrPdlA4ZzeeDACOtdxsVSyUHidilWNPln5SCJ+TvN51u6iKC1UA4SpaZLWxwLJNbrXquQVjs4WQQSoca4XUpKbARBEAQBEUiWPxGrTKDrzMUJaXXiIAFnV1ATEcA6QTtw59zGVc5B4m8kMh4CyDpIVgXrmIilGI+V6LAxbJURdJRwkMTCRSewhYSmkqzzW1eBizhIAP7pFVv5zzeci9c9f/+ep61o5ujoFFOJEg6KEgJJKp2hfzw2/x1swLmDJNCMKzHBhvYG9uV2sklGGY57aA54uPLMfKHloo1tdDT6soG4ZfH4TaBu6EjJVZLpDCOT8ZklNrMsiWgMGKfCpB3UGh8v6qryxkeZdFcI71XK/H/OUiABaK33MjY583/j/gPDnNc8bpwVTWVyUCxWtgSqDmntD8dIN3QCGiaHZq6wUA4SmP5McZoBUmsHSVIcJML84HW7pMRGEARBEBCBZPljl9h0ng5TJUIM5xPbOVEugwScBbXarXnrWuHoHyBevM1sMQ4MTvDe7z06PeGPR8o6SGKT43Q0+rKdNVa3mnGWDGodsjvYlHCQ6AwkyjgyLMLRJN0ea70SAslCcNrKJrQmX1DIpYRAMjgRJ6NZoBIb5w4SYuNsWdHI/sHp1yAVi9A/pbjunFX5wamYq6VXn7WSe/YMlRaJcgmuL+sgGZyIozWlS2yqnNA2+I1Akg1qhRllNlpr6pIh4v62yjucq0DS4JtRYjMeS7KzJ8SZ9SFoXg1uT8X9dLfUzSKkNYbLDjSOFAnWXSgHCczCQWK3+a1Vic3sHEmCUAnjIJESG0EQBEEQgWS5Exkw5TVNK40Do5pA1FpQSweJ3cr0jOtMWc7h3zkexm+eHOCXO/u462lLMCqbQdJAOjaRDWgFso6Ikq1+h/eaMoW2U2Yus8uLHOSQjE0lWOm2JvH1ZXIj5pnTV5pjs7e/RFCrt75osKR99X/VfJfYJKMQD8/o9FMUfzPEJ9jS1cThkSlTlgKMj4eJaD+vPr94yO2Lt3YTTaa5d28RV0IhwXVlHST2pL9kiU25EpgiNOUKJG2nmPdKQVDreDRFkHHSgQUQSOq9M0ps/nhwhIyGNWq4YnmNjXGQxNAOQ5jjqTTDkTi+llXmjkiRkqgFdZBUKZD4LIGkRLla1dglNl4RSITa4nUrcZAIgiAIAiKQLH8ig6Ybih3SuNBlNrGQmZh4/MWXz8ZBsuUqMwGoIofkmSEzAcmWTJQRSFLeBtzJKbauCWbvywok5Rwk7ZtMF5xC6qz9OMghCU0l6XCNQ12boyvu88W6tnrqvO7SQa2+RiOQFGTHHA+Z13HVfJfY2BNhpw6SdJzTOnykM5rDI+a9MDU5gdvfwPY1xcW7iza20dbgsMymaaXpqlOCgXFzXGpVYpPnIFHKlNkUOEgGJmK0qQlnQtucBRIfYwVdbH5/YJg6r5vGaO90p58KdLcEiCbTjEcduHaAwfE4AA3ttkBSwkHi9oNrAb7Osg4Sh6G7HglpFZYHbpciLQKJIAiCIIhAsuyxcxrsSdJCd7KJhUu7R6A6B0nc7u7SARufCwfucNzu9+CQubJ6z95BpmJxE4pYQiAJpXw0qCjbVk+Pu6PRj8/tKi+QFMsfgaocJOGpJG06tKjlNQAul+LUFY3sLSmQ2OGS+S6SPuv4dBeWktQaeyLsRCCx3n9bgua9sn8gwoHBCCoVZWVHW8mgW4/bxdVnreTuPYPEkhVChH2NkI6XDBu2g0dnlNjMNqQ1kCOQgCmzGT2YF8Q8MB6jnXE8TQ4FEoedlorR2mBCWnOdH78/OMIl6xtQkf6KHWxssq1+x50JBvZxbe0yLZqLCiTJ2MKU18D0Z4pTB4nLZT4Da9XmN/t+kja/Qm3xuFwkJaRVEARBEEQgWfZEBswksr7d/L3QnWwqCSSzcZD4m2HLFSbzYeRgxc201hwcinDqikZiyQy/233Y2k/xScxg3EsDMbbmOAtcLsWqYKB4iU0qAaPPQOdpxQdQ12puKzhIkukME/EULZnwogskYHJISgskdmlA/sSuNxSlKeChKeCd38FlBRInbX7N67yxMY1Lwb6BCX7yaA/1xFm3srx48OKt3UwlHJTZVCiVGBiP4fO4aK0vOC62MFilgyRbYhOzBZKLzW3Pw9l1hkdD1KkEgRYHx6guOOcSm2RaZwWb/nCMA4MRrlptld1UUWIDOO5kY5d0rWgPgr+leIlNKrow5TWQI5A4DGmFkuVqs8LuhuM0JFYQHOJxS5tfQRAEQQARSJY3Whvbf2NXTonNEnOQVNPFwc4g8TfB5ivM7wcql9kMReJMxFK87sJ1dDT6uO/JQ9P7KUJv1EODis0oh1jdWqLV7+hB0OniAa0wXWITHSs7znDUlCg0psaKtvhdaE5b2czIZIKhifjMhfYELJEf4tobjrFqQQJaq3CQWOUO/vQk69rq2ds/wU8f7aFRxalvKH+l/1mntNFa761cZpMN2yw+0e0Px1jR7J/pVsmW2FR3zOwSm0nbQbLqHNMpJqfMZmLUdLBqaF1ZeYdzLLEJ1pvSspBVZvPAQSPEXtxmTdgdCiS286gv5EwgyWa7tNSZPBq7a1cuC+kgqTakFSyBREJahaWN2yUZJIIgCIIAIpAsb2JhY/tfyg4SzywcJIFmaN0A7Vsctfu180dOXdHI1WetZOeBY2ZBiUnMkQnwkTLOkBxWtdQV72Jjd7CZY4mNPbmsS44uCQfJdFBrERdJCUGgLxyle74DWsFyCihn+Rr26xwfZ3NXE3ftGWBoPIqXZMUwS7vM5q6nB8qX2dgT0hIOkv7xGN3NRUSQxKQpsXC5Zy4rw4wSG28ddG/PC2qNhoxY4Gt28F4KtBinRaqIGOaANksgGbOCWu8/MExbg491LuvzxqFA0tnkx6Wg32Gr32NjUzQFPDT6PeZzbtEdJLMQSHw1dJDY77+Fer7CSYPX5SIlJTaCIAiCIALJsiY3yDIQNF1WaukgScYqX/ms6CCpJoPEcpDYV2k3XwGH76+4rZ0/sqmzkWu3duNNWa4H38xJzEQsSc+UNVktcEesbq1jcCKe7YKSZXgfoEoLJL4Gc3W/QolNOJrASwpfcnxJCCSnWQLJnmKdbEoIAr2h2PwHtIJxkDR0OAuyDViBmfEJtqxoJJnWdNdZr6GDrIYXbe1mMpHmvn1lymy8lUtsVhTLZUlOVV1eA9DgKxBIwJTZHH8E0kZoi49b4oSjkNaguY2V6Fpkk4zNKKsCaG0wpUOjkyaH5IEDIzx7Uzuu8FFweaGpu/IYAK/bRWeT33GJzdN9E5yx0np9G7sWP4Ok2pBWMOJWkWM6KxKTRvRbiEBa4aRCHCSCIAiCYJCzrOVMbk6DywX1bbXtYvOTt8OP3lp+HachrU4dJL6m6avtW64w2x3+fdnNDg5OUud1s7I5wMUb2+iusyaVRa7yPtk7TkRbYyoUSIJ1aD2de5BlaA8E15aebCtlJqAVHCRjk0nasCaoS0Ag6Wj009HoK97JJltiMy0IxJJpRicTrJrvgFaA8HHTOcYJ9uscG+fUFWbcrzjbyoVxUNpyyaZ2Wuq83La7SPmGTYnQWjAZOP3hGCubi3RySkZnVQ7hdinqfe7pDBIwQa2pGPTvBCATsQSdBochrVC5zOb//gK+//oZd+eW2BwcmqR/PMalmzpg7Ai0rKnKIbOypY7+8cqfB+mM5um+cc5cZQskpRwksYVzVDSuMEJ0nYPWyja1zCBJTEpAqzAveNyKlGSQCIIgCIIIJMsaWyCxJ5L1HbUtsRneBwfvKn3VWevaZ5DkihrrLzUCS4UckoNDEU7pbMDlUnjcLi5ZYyZzcffMicSunjCTtkASn+kgAWYGtQ7tg44SAa02dcGKDpJQNEmHWjoCCZQJai0SSmqXH827g0Rr45To3u5sfb/1/otPcPHGdk5f2cSrt1kT2AolNmBcDVeduYI7nxognipRZlOmxCYcTRJPZWa2+LXXrzJ/xKbB72EykSOQrLnI3FplNq6o5RardzBZdyqQHHnA5Jyk89vw5pbY/P6A+Yx5zuYOE6TssLzGZlVLwJGD5MjIJFOJdI5A0mVEzYL/W5LRhXOQbH0NvOseaGh3vk0tM0iSU5I/IswLHmnzKwiCIAiACCTLm2yJjdXFor4dpkZru/9MCg7dV3x5csosr5mDZHy6XALMxHLDZRVzSA4ORdjUOd3V4cJVphzgwd7kjHV3Hg8TaLDGWzDZXW1N/POCWjNpIxSV6mBj48BBEppK0KHsVsZLRCBZ0cy+gYmZJ8ZFBAF7Uts93yGtIwchOjrduaUS2QySMKuCdfz6A89lg62zObzafu22bibiKe7fX0JgLBPSarshZrT4tdefRYkNmE42E7kOkpbV0LIWjj1IJqPxxUdJK/d0+Uw5sgJJqPQ6k8Mw0Wv+V0f25y1qrvOiFIxNGoFkbVsd69rrZyWQrGwJZMNXy/FUnxETz+zOcZAATBa4SBbSQeLxORfubLx1NXaQSAcbofZ4JINEEARBEAARSJY3kX5w+6YnSA3ttSuxSSWmJ1OlHBz21ehaOUji4zPLYjZfASMHYPRQ8SEk0xwPRfMEkk3N5iTvtn2RGevv6gnR3WmVJCTynRPdLXUoVSCQhI6YINxKAokDB0k4mpwWSJZAFxswQa3xVIYjIwXOCNt5kSzmIJnnq/V2pxanAonHZ4S4eM7rmW2v6+xq+6WbOmgOeLh1V4kym6xgVEQgsTutFHWQzP6Kf4PfM93FxmbtRXDsIcamErToCeLeVlPiVQknDhKrdMf8vitvkdulaKnzMjyZ4A/PjJjymmTUiBXB9Q6fkaG7JUAknmI8NlPAzOWp3nG8bsWpK6zPBFsgmSjIIVlIB8lsqGmJTWTWgpsglENKbARBEATBIALJciYyaNXEWxOkWpbY2GGvygUH7jJlD4XYjgm7zW0xqs0gKQw/3HKluS3hIjk0PInWcErn9CTUbWWL3LovklcyEZ5KcnhkirXdluOmwKrv87joavLnl9jYHWxKtfi1cZJBMpVgtc8SHJaKg6RUJ5siDpLeUBmnRC059qA5nu1bnG/jb8ovBbOFHYflLT6Pi0s2dfDIkRIOrKyDZGaJzb4Bc+w2dhQRQpJTsy6xafR78kNawYhG48cZ6T1EuxonFXCYhZENaS0nkFiiiMuTL5ZYtNX7+N3+ISZiKS7d3AEhq1tUa3UCyUrLgVTJRfJkr+lK5PNYX1O2QFIY1JpcwC42s8FXyza/UmIjzA8S0ioIgiAIBhFIljORgenyGjBhjdExUxYyV2wb+6YXQvjYtFCQixMHiVJGJJlNBglA2ymm5e+Bu4puktvBJkt8nLS7jnBc87t904LR7l4z3k2rrWNWJE9iVbAu30FSpsWv1nq6NMVJBslUklWeiDkeS8Qmf+qKJpRiZlBrtmvL9JXvvnCUjkY/fk91LWur5thDxilRTacOf3O+g8QedxWBllvXtHB4ZIrwVBFnQ5FMFpudPWFWB+tobywW0jr7EpsGv4fQVJLB8Vj2Z7TtHADC++6nTU1Mt/euhJMSm76dpoRnxdnm9wKC9V6OjZr/jUs2tRt3FVRdYtNtCWyVckie6hufLq+BHIGksMRmGThIatnFRgQSYR7wuJSU2AiCIAgCIpAsb2wHiU19O6CNSDJXJq0OGdvfYG6LOTicCCRgBAGnDpJAgYNEKdh8pclBScVnbHJwcBKlCq7exydw1TXTUufl1t192bt39pjxnrrWakmamFmCszpYly0lAUz+SOOKoi6Znzx6nAs/cyexZNpcoY+FoYxFORxNssJttfh1UhaxANT53Gxob5jpIHG5rInd9DHqDcdYPd/lNdEQDD1tBJJq8DdNt4mG6ZIGhyU2ANvWmPexLaTl4S1dYrPreDi77QzmcMW/td7L/sEIF/3TXdM/X+9nSvvZ/cfbaWUCb7NDJ5K3zrTjreQgWbkNureZ3wtcY61WUOsZ3c1GDJqlQGKXIvUXdovKYXAixtBEfDqgFUwYrXIXcZDEpp1qSxE7g6SYC69akiKQCPODx+0SB4kgCIIgAJ7FHoAwByIDsObC6b/tq8mTw85af5bDLtVZda4pLzlwJ1zy3vx1sgJJsPy+vHVVZJA0z7x/8xXw8Nfg6B/glOfnLXpmOMLqYB11vhxXQyKC8jdx5foV/ObJfuKpNH6Pm13HQ6xrq6cl2JZdr5DVrXXc/uQAmYzG5VIwdti4WIpwz95BRicT9IVjbKwLgs6YXJMSglFoKkk740umvMbmtBVN7B0o1smmYUYXm82d8+x86dlhbp3mj9gEChwkyVk4SFab121nT9iUkOTi9oDbP6PEJjyV5MjIFK+7cG3xnSYnZ+0g+cCVp3LOuuCM+8cf3MZ16R6ap6bwtqyYuWExlDLvy1ICSWLKBLOe9Qrz2fHot2G81wTDWrQ2GIHkOZutz5nQUSO6NDpsx2yxojmAUuUdJE/1GrHrrFyBxOU2/zu5AonWloNkCZfYeOtBpyGdAE8Rl1E1iINEmCdMFxvJIBEEQRAEcZAsV9IpI2LkOkhsUaQWQa22jb2hwwgUR34/s7ygGgdJJYEknTKT2mICycbLTBjt/plhsabFb8GkPT4B/iau3bqSiViKBw6YPJWdPWG2rmkx41Gume1CgTXBOhLpDMMRy60S7oHm1TPWA3jsiHHq9Iai0yJRmRySsakErYSXnkCysonDI5NEEwWlWTnhklpr+kJRuhcioFW5YdV51W3nb87PILGdHlWIE8F6H+va6tl1PFR8Bd/MUoldx83/wLbVweLbJGc/eV8drONNF6+f8bPyrOfSPr4HbzLsvMQGygskg08ZgW/lVvMDM3JIWutNd6hLbPEodBSCa6srhcLkvXQ0+stmkNgdbM7oLvg8aOzKL7GxXWVL2kFSugNS1SRmL7gJQjncUmIjCIIgCIAIJMuXySFA52eQ2JMlO2B1rvt3+83Ec/MV5urn4fvz17EnW8VEjVy8dZVLbOzyiMIMEjBXTNdfMiOHJJPRHBycZFNnwRXV+AT4Grl0cwdNAQ+/2tXH6GSCnrEo21a3mKvpvqaiDpJVua1+M5kZV9Ft+sMxeq0JXm8oOl2CUybjITyVpCU9tuQEktNXNqE17B8sDGptzIpi49EUk4l0thXyvHHsQVh5NvirdKoUZpBkQ1qrm0xuXdOSLcWagbdhxiR3pyWm2O6TPDIZs36tr/ivvdg4EqA6p1g5gcQWQ1ZuhRVnAWpGJ5tNnY201nu5aIPlwAodrbqDjU13SyD7/1OMp3rHWdNaR0udN39B44p8B0nK7la0hEWDajp5lSP7floa+UXCiYVXSmwEQRAEARCBZPliTxLyMkisyVItOtlMDk9nZay/xExACh0csZC53+Mrvy8nDhJ7cluYQWKz+QqTTRHuyd7VPx4jmkznB7Ta+/I34/e4ufKMFdz+ZD+PWm6PrXZWhK+hZIkNWALJ5BBkktC8ZsZ6jx2dznnpC8cqOkiS6QwT8SQNqbEl0+LXxu5kMyOoNafEptfKi+humUeBJJ2C449UX14DVgZJzuQ/GTUuoSpLGratbqFnLMroZGLmQl/9DBfVrp4w69vraan3zlx/vibvxcrqnFBWINlllgfXmWPZdsoMB8lrL1jLAzdcToPfqswcO1J1/ojNyuZA2QySp/rG88trbJoKBJKkJbIs5ZDWMi2iq2IWZWOC4BS3S02HjguCIAjCScy8CiRKqWuUUnuVUgeUUjcUWf4FpdTj1s8+pVQoZ9lblVL7rZ+35tx/r7VPe7uuwv2eFNg28xkhrdTOQWJfnfb4YeNzZwa1xsKVy2tg7g4SMEGtkDeGZ4bMZHWmQDLdDefard2Mx1L8930HATjbvtLvbyxaYmM7JI6PRWHcEmOKOEgeOxbC53YRrPc6cpCEo0mamcKtU0vOQbK+vYGA18XTfeP5C3IEgT5bIJnPEpvBp4xoNWuBZGI6CDNhdY+pMgzXFtDs0pk8ckqObHb2hIu7R+wxQO0dJPVt012VaiWQ9O00Aa328Vq5dUYnG5dLTWf9JCZNKd8sBZLulkDJDJLJeIpDw5Oc2V3kuDauMJ99dlaCLUIt5Ta/WQdJrQQSySARao/XrUimJYNEEARBEOZNIFFKuYH/Al4EnAm8QSl1Zu46Wuu/0lqfo7U+B/hP4KfWtm3AJ4CLgYuATyilWnM2fZO9nda6oOfjIpOKm44r803WQZKjD3l8ptSgJg6Swfx9b74Cxg7ByMHp+5wKJNU4SEqV63SeZpwcOQJJtsVvV2GJTSQrkDxnSweNfg8PHx7jlI4GmgPWlf6c8pFcmgJemgMe4yAJHzd3FskgeezoGGevbmZdW70pFajgIAlNJelQ1uR0iQkkbpfi2ae0c/OOnqwQAuQdo+MhM5md1xKbYw+a22o72IBxHunM9Gs6y3BUW0Db1ROaubAgtHYkEud4KFq6g022k848HDP7GNWixCaThoEnp7NHwPweOlI6Uyd0zNzOssRmZUsdE7EUkXhqxrI9/RNoTX4HG5vGFaa8KDpq/l4ODpJsBskcS2xsx1sVnZkEwSniIBEEQRAEw3w6SC4CDmitn9FaJ4AfAC8rs/4bgO9bv18N3KG1HtVajwF3ANfM41hrx1M/h2+91HQ/mU+KldiAuaJcEwfJcP5EfvMV5jbXRVJLB4kdsFlKIFEKNj0fDv8+6xI4OBShye+hszGnjELrbEgrQMDr5oozjNCzNXciW6LEBkwOSW8oCuOWQNKSX2KTSGXY2RPm3HWtrGqpc+ggSZgONrDkBBKAT153FqmM5qM/3YW2XRje+myWR18oisel6GisrmSlKo49BE3d0FKiI0w5bOeRLbQlpmZVitAc8HJKR0PxHBJvfomN7TLZWjKgtfqgWMdsudrk6LTMLP8qSV2wuEAyctA4MVZum76ve7u5HXiy+L5CR83tLB0kq4J2q9+Znwt2QGvREhtbtLU//5aFg8QWSGYKslVhv/fEQSLMAx6XSxwkgiAIgsD8CiSrgWM5f/dY981AKbUe2Ajc7XDbm6zymr9XqkoP/XwzOWRubffBfBEZNOJE4ZXTho65d7HROr/EBqBtI7Rtys8hiYUqt/gFZ21+K2WQAKw421w5thwyB4cinNLVSN5bIBU3uSE5IZ8v2toNFARp+puKltgArGmto2csavJOPIEZZQx7+seJpzKcuy5IdzBAXyiK9jaY7itlHCTtaukKJOvbG/jw1adx794hfvKo9d7NcUz0hWOsbAngds3jv9uxB40zYjb/0rawZpdqJadmfaV965qW4iU2vvyQ1l2WiHL26hLv2fkqsQE48zr4yCFnAqVNoMUIlckCUSI3oNUm28kmP6g1S+iIuW2dpYOkuYxA0hsmWO+lu6WIK8QWhG2BZFk4SGoU0jqf7yfhpMcjDhJBEARBAJZOSOvrgZu1tlszlOVNWuutwGXWz1uKraSUepdSaodSasfQ0FANh1oBe4KWGyQ4H0T6Z7pHwAS1Ts7RQRILm641DQXxLluuNJ1s7BP9akpsKmaQ2B1xSmSQwHTuwvBegNIdbCDPifLC07v4qytO5eXn5mhsZRwkq4N1psRm/Dg0r5oxYX/saAiA89a1sjpYx2QizXg8bV2hDxXd59gSLrGxuf6SDVywvpVP/+JJBsZj1jEyk7LjoSir5jOgdaLfTLpnkz8COQKJ9fonp2Zd2rJ1dQt94RiDEwXv2ZzjAbDzeJhTOhtossu2CplPBwmAu8TjlsL+Xy10kfTvNG20O0+bvq9xhXmfFgS1ZgkdMV2uCj8jHGKH/fYWCWp9qnecM7ubKap9ZwUSq7JyOThIbEGjViU2IpAI84DbraSLjSAIgiAwvwLJcSDXK7/Guq8Yr2e6vKbstlpr+3YC+B6mlGcGWusbtdYXaK0v6OxcwAmpPfmIzHM0SmSwhEBSgxIbO8OkcCK/+QozITnye/N3NSU2c80gAeg83dwO7SEST9E/Hise0Ap5QovX7eL9V2zJLw/xNZYtsZmIpUiFeormjzx6dIwVzX66WwLTE71Q1LhpomMz1gcITSXoUGE0qrpgzQXE5VL866u3EU9l+LtbdqPtEptMhr5wdH4DWo89ZG5nK5DYziP7/2+WJTYA29YEAdhd6CLJKTkC4yDZViqgFeZfIKkW2+01QyDZBV1n5AsuSpmSm5ICyVEIrgXX7L5CuprN/2KhgySVzrCnf6J4eQ0sbwdJkcyjqpCQVmEe8bqkza8gCIIgwPwKJA8DW5RSG5VSPowI8vPClZRSpwOtwB9y7v4NcJVSqtUKZ70K+I1SyqOU6rC28wIvAXbP43OonthCOUgG8kNUbRraTYmNnsOJjl0mVBgAueE5xg1y4C6z/1o6SGLjpkSl3FX/5lUmd2FoH4eyHWwKJgu26FHOiQKmBKfEhMVu9atDPUUzHh47GuLcta0opbKiQV/YyiEpUWITjibpUOOmA4nbU35si8gpnY389VWncufTAzw1YgxdmcQk/eEYq+Y7oNXtz8/BqIbCDJLk5KxLbM5a1YxSzMwh8dVnHSSD4zH6x2NstcSUomQzI5aKQFLEQaK11cFm68z1V26FwT2QKtLyOHR01vkjYLKB2ht8MzrZHBqeJJ7KFA9oBfN/622ACTuD5P+zd9/hbZ3n/f/fDwb3lihSErX38pDlFduNRzwymr2TOs2eTWfapCtt+k2b7l+apmmSZrlpprPsxIm3Yzve8pC19xaHxD2xzu+P5xwAJAEQJACSED+v69J1yIODg8MpPve5h/v82ZxBkrcmrQOjzyeSR16TVieXvx1ERETOAwULkDiOEwE+hg127AF+4DjOLmPMZ4wxr0469K3A95yk/5Udx+kE/g4bZHka+Iy7rxQbKNkBPI/NKvlqoT6GKfFKRQoeIEmXQTLflsd4C8WpiAdIxmSQBMth2VW2D0mo304NmUyT1kx/eI302SyATP0njIH5a6Bjb2KCzbgMEi8TZYIASUmVvSMbG1/VtbiuHD9RAoNt4zJIzvaPcLxzkK3L6uLHApzudifZpCmx6R4M0+zvw8zS8ppk7716JRctqeNnu+338rmuLsJRh0WpekLky4mnYPFWO4lpKsb1IBmacmCisjTA6saqeI+RuGAlREcgGon3KEk7wQZmYQZJigBJX6sNqKYKTDVvsf18OvaOf6z7+JQn2MRPX1tG65gSm12n7dcv5YhfT9WCpAwS9/mzOoPEC5DkOOY3XmJTlfk4kSkIuP2llEUiIiJzXUF7kDiOc5fjOGsdx1nlOM5n3X1/7TjOHUnH/I3jOJ9M8dyvO46z2v33DXffgOM4lziOc4HjOJscx/n9LPuWTJ/pyCAZ6bd/LKfKIPHKN3Jp1DrglgelOv/ql8G5A/auM6QMkERjDvfvaSMUcTviB9zFS6YskpHeiYMaYMtszu7nUEc/fp9h6bwxi8/JBEggZZnN4vpyFtCNcWJQOzpA4vUfuXipnTo9v6qUgM8kJtmkySDpGgyxwNc3a/uPJPP7DP/8xgvoDttgRUenLRsqWAZJeBjOPA8tl079HKmm2OQwXndLSy07TvWMvptakphGsuNkDz4DGxdmKAmLL95nW4CkO7HPa8KaKkDiTbIZ26h1pN+W8eWQQQK2D8nYDJLdZ3opCfhYOTYzLFlVU9IUmyLIIAmUAiZzgOQXfwI/+XDm86hJqxRQwG//HFSjVhERmetmS5PW88fwNGSQxAMYzeMf88piBjtzOL8bXEnVK2PNjXa780d2myJA8pPnTvHebz3Dm778OCc6B7Ob4jDSB6VZZKM0roW+M5xubWNpQwWlAf/484AtxcnEW2SkKLOZX1nK0oD7+asZXWLz3PEuAj4Tn4jj9xmaasrsQq+8PsOY3zDzTE9RBEgA1jRVc90FKwD46VP7gURjzbw784LNeppq/xFIBEi8AGUOJTYAFyyupaNvhLbekcROL9ARGuTFUz2sXlBFZWmGcqliKLFpfcFumzaNP75hpf2YxwZIchzx61lYW0Zr75gAyele1jdXE/Rn+K+pakGix1MxZJAY4/avSfP7LxqGF74HB+7JfB6V2EgBKYNERETEUoAk3+JTbArYpNWrv0+ZQeIGSAZyySDpsIv9VFMy5q22C6PdP7XvpwiQ3L+njdryIIfb+3nVFx5lT4fbwyBTBsnwJDJIgGj73vH9RyBlk9aU4hkH4zNIfD7Dxko30JIig2TjohrKgonATHzqTVmdXXzGYuPO2T0Yps4pngAJwE0X2QDJM/tPArCoUE1aTzxpt0tS9lvOjs9vs4KSM0hyCEx4vUV2nOxO7HSzjpyQzSDZsrgu80m8jIHZkt2QMkDyItSvSD1e2+e3gZOxjVrjAZLcS2y6B8MMhWwSoOM47DrdkzkrB0ZnkISLYIoN2O/FdBkkJ56EUJ/N+ss0gSw8YIMjU2yMK5KJN8I9Eh3//5eIiMhcor+08s27gz3QkbK/RV54i4NUPUgq81Bi09+efnynMbD6xsSknDEBklAkxiMHzvKKLc3c+XtXs7iunK88fgaA8EiGFPOR3tSLtLHcUb9lPYdYObb/CCQCHjmU2ACsLuu2byT1IIlEY7xwspuLl9SNOnZhXVmiSasTs4udMfoHBqiM9RdVgMRfZj+Htf4RKkr81JZPcqxstk48aRfpqQJ+k1FaY3sARcO2d0YOGSQbF9bg95l4rxEgHnA529XF2f6RzP1HwN7xn00L2mC5bYQ7NkCyMENj3OYL7DHJpUZ5zCABt8Ex0No7TNdgOH2DVk9Vk83UiozYqVq+wKxufAzYz30oze+/g/cl3nZHmKfkfT+JFEDQrwwSERERUIAkvxzH7aVRYxfKuY7bTcfLTkk35hdye+2Bs5kX8qtflnh7TIDkmaOd9I9EuG7dApbPr+THH3kJl65ZBMCffffJ+GJonGx7kNQvx/GXssI5kSaDpG/iaTiQVGKTOkCyLNDFAGWjPr79bf0MhqLx/iOeRXXltPYME/NKhFL0IfEPuV+PquIJkHiLsd+9dAFv3rYEk6mB7lQ5jm3Qmkt5jae02n79481Rp55VUF7iZ82CqtGTbNzPx+HT9udvy0QBkvBQTtdQEGW1iTKw4V7oPJx6go2neYv92ew+ltjXfcz2FcoxoNXsBki8Ub+73QataUf8eqq9Ub/ttn/NbM8eAbfEJk2A5MB9NkAI0DFBgET9R6RA/D71IBEREQEFSPIrMmx7Kcxbbd/vay3M6/S32SBARcP4x0qq7F3iXEtsxo74Tbbit8DnZhOUjw4WPLC3nRK/j6tW2+eXBf28/ap1AJw628Ur/+NRHt7fMf6cI32JSSSZ+PwMVK9gtTk9foJN/DzVmafhgB0XCilLbACaOcfp2DxGktKNnzthm5VuHRsgqS0jHHXoxT3nmD4k4WiM0pDb06SIMki8xdhLl1fwN69O0aMiH7qO2p46uZTXeMpq7KI/3swyt7vtF7TU8mJyo1b383G89Sx+n5m4FCQ8mFMWS0GU1SYySNp22W2m0creY2eSymy6j9nskRwDZl5PG69R667TvRgD65qzyCAB+3swMjS7+4940gVIes9A24uw9VZ7zIQBEk2wkcIIuBkkYZXYiIjIHKcAST555TXz19htofqQ9LfZhbbPP/4xY2xwI6cMkvbMd4dLq2DZle7boxczD+xr5/KVDaObV7pTbP7tdWuZX1XCu77xFHe9eCbxuONk34MEaCtZyhpzMnOAZCLxEpvxTVoBGiIdnHEa4ne3AZ491s28yhKWNIy+Y+0t9M5G3f1jMkh6h8LMN+73RhEGSNJ9jiY02An3/Q2cPZD+mBNP2W1BMkhyC05saamjcyBk+8tAPIPkdMc51jZVj+pDk1JoYPY0aPUkB0jiE2wyZJAs2ADGN7pRa/fxnMtrIFFi4zVq3X26l+XzKqnK1PgWEr+b+ttsBslsy9JJJV2T1kP32+2am+z/GxOV2My27yc5b3hNWpVBIiIic50CJPk0MjZAUqBJNv3tiTTzVCoapp5BEgnZBdREC/lL3w+bXjeq9v/o2QEOdwxw/foxwRV3AbO4yvCzj17NhuYa/v6uPYkxwJER2zMimx4kwGGzhBbfWepLUvR4ybZUJx4gGd8vBKBqpI3TzjxOdSUWNc+d6OLipXXjSk288bdnRtw72WMySLoGw8zHXZRmysyZbXIJkHQdha/dBI/+O3ztRjj+ZOrjTjxpJw4t2DDly4wrrbFf/3CeMkjcSUUvemU27uej49y5+GMZhQdnX8+IUQGSF2xT5+qF6Y8vqbB9fwoQICkL+qmvCMbL7naf6Z24/wiMzyApihKb8tQZJAfvs5//pk22AXWmDJLwoEpspGC8Jq3hqAIkIiIytylAkk9eBsm8QgdIWlP3H/FU5JBB4jV3nWghv/HV8KZvjtr1wF6bMTMuQOJmkBAeorzEz5/eso6TXUN89ym32WN88kx2AZIXR5rw4aTOTMg2gyRTiU1khJLhs5xx5nHSzR7oHgxxuGNgXP8RSEx3OTlcYneMySDpGQrZEb+QvvntbBQfazvJAMnp5+B/brSZSK/7ii3Duu3VsPuO8ceeeApatqXOhposL4MklHsPEoD1C6sJ+g07To0OkDihwYn7j4Dbg2Q2B0hetNkjE5XKNG9JBEiGe2GoKy8BEoDm2nLOdA/TOxzmeOfgxGVLkAjeej1IiqHEpqRifJPWaAQOPQCrb7Bfg/lrofdU4v+RsUK5ja4WycQbra0MEhERmesUIMknL3OgutneFS9Yic0EJTCV86c+xWbA7Q8yhVKQB/e1s6qxkmXzxvwR7y1U3TG/L13byGUrGvjCAwcZDEUSo1mzDJA80edeW6q7raH+7AIkwQzZEb2nATjDPE67AZLnTnQDcPHSunGH15YHKQ/6OT7oBkjGZJB0D4aZZ3qJ+cuK6w6wz2/vzocnESA5cC9845UQKIX33AMXvgXee69dZP/gVnjyy4ljh3uhfVd+ymvAXfz3Jq43x8VkacDPuubqRAaJG+woJ4sJNjA7SyK8AEk0DO17MpfXeJq3QO9JWzLVc8Luy3HEr2dhbRlneobZ4zZozSqDxB+0zaiLKoMkRYnNqe32a+E1vXZHmKctSQv1F9fvDykq8TG/KcbUi4iIzCUKkORTciZE1YLCZJDEYm6AJFMGyTwYmGIGSb8XIJlcpsPASIQnD3eOzx6BURkkAMYY/uyWdZztH+EbvzmauKOdRWCjZzDMcwPziBl/6nr9bDNIfD67gE41xab3FABD5c3xEpvnjnfjM3BhS924w40xLKor42ivsc1zx2SQdA+GmW96iFXMz7mx5bQrqcw+g+TZ2+A7b4F5K21QZIG74KucD7feAeteAb/8U7jnr+z38antdtpTPhq0gv26hwcSWUF5CE5sWVzHjpPdtlGruzit9o2wrjmL77HZXGLTsc82lF544cTP8Rq1tu6ALneaTZ4CJM21ZbT2DrP7jDvBJpsMEoCq5uLKIEnVpPXgvfb3xcrr7PuNtpl12j4kIZXYSOHEx/yqxEZEROY4BUjyyUuNLqu1WSSFCJAMdYITnbjEJtRne3tMVjyDZHK9Mh49eJZQNMZ1qQIkYzJIAC5Z1sDLNizgv399iP5eOx0mmx4kh872EybAUNVS6Ng7/oCRvuwnPZRWpQ6Q9NgACTWL4w06nzvexbrmmtHNZ5MsqivndO8IlNel6EESYj69OY9FnRGpSgPGchx48B/gjt+DldfCu38JNWP6WpRUwFv+Fy59Hzz2H/Dj98HRRwBjS2zywctA8n7u8lCOcEFLLb3DEY53DoLPT4ggiysdSgNZlATNxgVtWa0NjJx0m+Nmm0ECtsym2y2Ly1OJzcKaMjoHQjx3vJv5VaUsqMky2FG1wE4JK5oMkhQ9SA7eBy2X2t8ZYEf9+oLp+5BozK8UkDfmN6ISGxERmeMUIMknL4OkrIAZJN45M5bYzLPbqfQhmWKJzYN726kuDXDp8hSjh70AyZgU8z+5eR39IxHuedZNKc8i8+NQuxvQaFwHHfvHH5DtuGCwi41UPUh6T9rLbljC6e4hYjGH5493pyyv8SyqLedM9xCU1aXoQWJLbPxVRTTBxlOSJojkiYbhjo/Brz8HF70T3v799F9Hnx9e8S/wsr+FnT+CR/4NFmy0i/Z88F7XG6+dh+kmW9xmrDtO2nG/g5SyuDLLBUR4cPZNWPEW40cftYEFbyR5JpXzoXqRHfXbfdw+L0/Nhhe6DY5/vb8ju/IaT1VTcWaQeCOj+ztsr541L0sc4w/Yr0eqAEkspiatUlDeFJuIxvyKiMgcpwBJPg332JGYJVWJP+DzLR4gaU5/TIW7eJnKJJuBdlsSk+XIXQDHcXhwXzvXrJ0fb/Q2SmB8BgnA+uYaXnPhIp7ce9TuyCKwcahjgKDfUL5wA3Qesgt0TyyWfQ8SSL/47zkFZXU0zmvgdPcwB9r76RuJcPGSurSnWlhXRkf/CLGyupQ9SBb4ejBFGSCZoMTmuf+F574NL/0zeM1/2v4QmRgDV/8BvP5/wBeAlS/N37V6GUj9boAkDyU2a5uqKQn4ePFUD8fODTLglNJUlmJ6UiqztcQG4MgjdnJKts1xF17gZpAcs9kjeSoV80b99gyFs2vQ6vEC0JHh4sgg8b4Xvd+Bhx6w29UvG31c49rUJTaRIcCZfd9Pct7QmF8RERFLAZJ8GnZHzBpj/4Af6Z24PGGy+rLIIKnwMkimEiA5a7NHJrEA2nW6l7beEa5bl+aafD7wl4xvUgj84Y1rqXTcz1EWAZLDHf0sn1eJb8EGiEWg83DiQS/YMakASaomraegtoWWunJC0Rj37rYL7q3Lxk+w8SyqLcdxYCRQnaIHSYgGeotrgo0nVe+EZPvvtqUB1/355BbNF7wJ/uBFuOGvc79GTzyDJH8lNiUBHxsW1rDjZDc7TvUw5JQyryQ88RNjUbsYnm13/L0AyUB7duU1nuYtcHa//Vefn/4jYHuQeDZNNoMkOmKDJMWSQQKJ/w8O3mt/zzaP6QHTuN6OyA6PDibHf0/Ntu8nOW8EvB4kCpCIiMgcpwBJPo30JhYgXo+QgTxnkcQzSDL0IPHS3wc7J3/+gY5Jp88/6I73vTZdgATsXd4xGSQAy+ZV8pIWO/3l2MDE346HOvpZ1Vhl77TC6HT0+DScLAMkpVWJ5yTrOQU1i1lcb+9M3/nCGWrLg6wYO50nySK3VGDAV2XHoCYJDXQSIDqlyUAzLl0QCewi7sjD4++CZ6tmYX5LUErdn73+Vtv8cqJslixdsLiWnad6eeFEN0OmjGp/aOIneUGl2VZiU1aXeHuyARInagMkeeo/AtCc1HNk0iU2YPupFEMGSbzMcNAGzw7eD6tusMHjZPPX2sbF5w6O3h8PkGTZX0lkkgLxHiQqsRERkblNAZJ8Gu5JLNK8P+DzXWbT327vjJdm+EM5lxKb/vZJZzo8sK+dC5fU0Vhdmv6gYFnKDBKAKxcHGXaC/NsDRzO+Tjga49i5QVY2VtqFBKQJkGS5iEibQXISahfHgx772vq4aEkdPl/6DImFdXah10vluBIb430dijJAUpE+QHL8cbvgW3Pj9F5TOsk9SEoq81YGsqWllv6RCL/YcQZTUokvzffxKN4xs60kIrnfSzYTbDzeJBvIa4CksjRATVmA8qCf5RkCkOMkZ9AVUwZJeAhOP2+bbaf6uYmP+h1TZhMPkMyy7yc5b8TH/GqKjYiIzHEKkOTTcG+iD4IXIPEaRubo3+7Zx0e/86zNIJloGkp5HWByK7HJ0rn+EZ4/0c31mbJHwPY1SZFBAlDJINGSKn72/Gl2n+5Ne4rjnYNEYo7NICmphNqloxcS8QDJJJq0ju1BEhq0GSA1i1lcl7gzvXVp+vIasCU2AJ2xShsoS7oLFxia2mSgWSFTD5KD99nSqeVXT+81peP97A2czWtg4oIWG1Ro7R2mpDxDRk2y2VoS4QVIjM82yM1W3bLEz1UeAyRgs682LKyOL9CyUp3Ug6koMki8AMmA/bnBJMb7Jpu32n5txjZq9TKSZtv3k5w3vBIb9SAREZG5TgGSfBrpSSwi4hkk+Zlkc8/uNn6x4wwjPWcyl9eAbbxY0TD5KTaOM+kSm4f2deA4cH2q8b7JguVpM0gY7qWsqp6asgD/ck+aEZckJtisWuBmiDSuHT3q15silHWJTfX4KTa97ojf2haqy4LUlNmxvpkm2ACUl/iprwjSES6zKfKhROlOybBb6lSUY36r0vcgOXgfLLtq9iza4l93J6932lc3VlEWtL8qyytrMvdk8cRLbGbZHX/v99O81ZP7HPl80LTZvp3nAMnfvXYzf/PqTZN7UtFlkCRN8jp4Lyy+JDFtbNRxZTYYNTZA4gVy89BXRyQVr8QmrACJiIjMcQqQ5NNwUg+Syvn2TmAeSmzC0RiHOuwfyIOdZ7JbaFfMn3yJzXAPxMKTyiB5YF87jdWlEzdYzJBBwkgf/rIaPvjSVTywt53bt5/kycPnxv179KD9eFY2uouExvVw9oCt6YcpNGl1M0icpD8Ie+yIX2oWA/butjFw0QQBEoCFteW0htyFkNuoNRyNURFxe5IUY4lN0C2xGVuX3n3CBqem2n+kEIIVtveI93aeBPw+Ni2yP9c1NbXZNV6erSU2wTL7sziZ/iMe7zl1+WvSCnDp8gYuaKmb3JPK6mz2EtiPZ7bzgog9p+DU9sw/N43rUwRIZmlGkpw3ElNs1INERETmtsBMX8B5Zbgnkebv89sgRR4ySA53DBB264KDg+2j08vTqZg3+QySAbcUJMtMh3A0xsP7O3j55uaM/TmAzBkkI7Y06d1XLedbjx3lT374QtrTLK4rp6bMbb45f60NunQfh4YVk2/SWlIFOPZuv7fwiGeQ2ADJ2qZqAn6TeM0MFtWVc7LNXbQNdwPL6B0K02h6cDCY8obsrms2KakEHDtmNHlxdvA+u50t/UfA9hwprbaf+zwHJi5d3sChjn6qq2vg+GRKbGZZgATg5s/Coq2Tf96l74OaRYkpWTPJGJtJ13Ni9jXCTcW7xn2/sBlmGQMka+3PVzQCfve/6JBKbKSwvBK3sHqQiIjIHKcASb44jl2gJ/e/qGrKSwbJ3lZbOvKaTfOoOtRPf7CBCduQVs6Djv2Te6GByfXK2H6si77hyMTlNWDv8o7t9+EZ6YPKRipKAvz0o1dx9Gz6BejSeUkLzsZ1dnt2/9QCJF4z19DA6Du8EM8g+bvXbiYcze6O2qK6Mo4ecQMpbgZJ91CYefQQKqmn1F+EP27e5yU0OD5AUrsk0Sx3tiirsQGSPAcmfv+GNbzrJcvwPfVodj1IZmuJDdhAx1Q0rk1Mj5oNqhbYAEkxZJB43wf774HyelicIUDVuN5m8nUdgflr7D7vd6cCJFIgQb9NKFYPEhERmeuKcMU2S4UG7BjMsuQAyYK8ZJDsa+0j4DN87PIaOAQvdJVy1URPqpgPg49P7oXiAZLsSkEe3NtO0G+4ek0WxwfL05f8DPfGA0uL6srj02MmlDzJZu3NiQBJyWQySLDP87Jmek/ajz9gJ/LUlmc/KnZhbTnPjJRBKfFJNt2DIeaZXiLl88gw42f2igdI+gH36xwJweFfw5Y35G1STN54Aco892ooL/FTXlJuv2eiodF391NRSUTheb2YZmMQaqzkJq3rbrEZhunMdwO/HfsSARI1aZUCi0+xUYBERETmOPUgyRevQWjyGM2qprwFSFY2VrKmwv6R/PCZLL5slfPtKMnJ1BN72S5Zjvl9YG87l6+YR1VpFnG2QJkt00hlpC/7rI9kFQ32Wr16/ZFeO9Ei20wNL0CSnNnScyqePTJZi+rK6HHcBYyXQTIYZp7pJVaRXVbOrOMtyJIbk558yjahXT2Lyms88QBJgcoukhe6mcR7kBRB+UexigdIiiGDJOn7YKKfGy9LJ3lClxdwK4ZgkBSloN8b86seJCIiMrcpQJIvwz12m1xiU+2W2OTY9Gxvax/rmmviwZbH2vy096VpeOqpmGdr3Ye6sn8hL8Mjix4DJzoHOdDez3XZlNdA+h4ksVi8B8mUNK5LLCRG+icXaIlnRyQtdntPQW3LlC5lUV053V7xUzyDJMx8ejDFOMEGEpkYyZ+jA/eCLwArfmtmrikT7+tfqN4f3nknatQaL7HRHf+C8QIkxTDmNznzY/UNmY8trbZB2uRGraF++3FmyjwRyYHfpzG/IiIioABJ/gx7GSRjepDEwvHF8lT0DYc51T3E+ubqeICkPVbH3TtbMz/Ry1iYTKPWgQ4ob8gqA+OBvTbbJKv+I2AzSFIFSMIDgDO1DBKwAZKOfUk9YCZxHu/YkXxlkJQzQBkx4x/Vg2S+6SVYM8Fo5tlqVImN6+D9sPTKqQe1CqmsMCU2ccEUGTWpzOYmrecLL+hYDBkk/qANKi68MLsm2PPXjgmQDKq8RgoqPuZXTVpFRGSOU4AkX7wSm9LkEhv3D+Ecymz2t9m+GuuaqqGvDTDUNS7irhcnCJBUulkgg5MY9TvQkfUEm58+f4q1TVWsmJ/lH+3B8tRjfuONVae42J6/zn7u+1onHyAZW2Iz3GNLR2qnFiBpqi7FZwzD/up4UKyvv58aM0iwpkgzSMZmTPSegbYXJ74LPlOmLYNkohKbQcAURwPRYrV4qw1C1y6Z6SvJzsIL4cK3ZXds43rbfNrLPgwNKNgmBRXwa8yviIgIKECSP16JzdgeJJBTgGRvqxsg8TJIKuZx85YWnjxyjrP9I+mf6JXJpGuMmspAR1YNWg+29/Pc8W7edMkkFiZeBokz5u6Ul3mTSwYJ2DKbSQdIxmRHjJlgM1kBv48F1WUM+KriGSThXptp46vKrvHtrFOSNOkH4ND9djsb+4/ANPYgmajEZsgeO9ua2J5PFl0Mf7I/66lbM+79D8AVH87u2Ma19nus96R9PzyQ+FkUKYCAmrSKiIgACpDkTzxAMqbEBnIa9bv3TB9VpQFa6svteaqaeMUFC4k5cPeuDFkk8RKbyQZIJl5s3L79JH6f4TUXL8r+3MFywLETQJJ5GSTJgaXJaEya+DDpEhtvio0bIOl1AyRT7EECSY1a3QwSJz4ZqFgzSLySEjdAcuBeqF4ITZtm7poy8b7+hSqxSVVylIru+EsuGtfbrTeqPTSgBq1SUMYY/D5DRCU2IiIyxylAki/xEpsxY37Bln9M0b7WPtY1V2OMsRkkVQtY11TNyvmV3PXimfRPrJxiD5IJMkiiMYefPHeS69Y1sqB6EuUD3h39sX1IRrzmtlPMIKlqssGVjn22PGZKJTbu4r/HvVs7xQwSgIV15XTGKuIZJGaSo5NnnWBSSUk0AocftOU1szUzwgu0FazExguQZNGkVQtamar4qN+9dqseJDIN/D6jDBIREZnzFCDJl+Fe24QvObW/tMZOHphiiY3jOOxt7bXlNRDPIDHG8PItzTxxuJNz6cpsAqVQUg0DWQZIIiM2C2aCTIeHD3TQ1jvCGy+ZZJaF14thbB+SXHuQGGMXE1PJIPEHwV9qAytgM0iMz2ZITNGi2jLaw+U4bgZJcMjN4CmWMoCxkif9nHrGfo+sftnMXlMmhc4gybbERnf8JReV82wWoDehK6QSGym8gM+oB4mIiMx5CpDky0ivvXudfGfdGJtFMsUSm9beYXqHI3aCjePEM0gAXrFlIdGYwz27MwRfKhqyL7EZyG4hf/szJ6mvCHL9+klOZUmXQZJrDxJIjPqdbIAEbJlNPIPkFFQ1ZzXFJ51FdeV0xSpw3AyS0lCnfaBYx/z6/Da4FRqAg/eB8cPK62b6qtIrdA+SVKOhUwkPqcRGcuNN6AJb0qXvJymwgM9oio2IiMx5CpDky3BP6iyIqqYpZ5DEG7Q2uVNRoiPxviYbF9awbF7FxGU22ZbYZFEK0j0Y4t7dbbzmosWUBCb5rTNRBkkuI2Mb19nrj4Ymf5e1pDKpB8nJKU+w8SysLaeHSsxwDzgO5eFOQr6y4k6PL6m0AYED90LLpVBeN9NXlJ7XnLhQ15h1k1aV2EiOvFG/jmO/n4r5d4gUhYDfR1QlNiIiMscpQJIvw72pF/k5ZJDscwMk65trEueobgZsQ7VXbFnIY4fO0TUQSn2CivkZp9g4jsPt209y9OxAIkCSIdPhjhdOE4rGeNO2KTQxTduDxM0gySV93KvXh8mX6pRUj55ik0P/EYDFdeX0OJUYJ0pkqIfqaDdDwYaczjnjSiqh+xiceX52l9cALLkM3v4DWPqSwpw/2x4koQEtaCU3jettYLy/3S3Z0veTFJbtQaISGxERmdsUIMmXkd68Z5Dsa+2juaaM2opg4hxJAYxXbLZlNvemK7OpmJcxg+TnO87wJz98gffd9gyhXvccGUpsbt9+kg0La9i0aAoTZzJlkJRU21KOqWpMDpBMssSmpNIGSBzH9iDJYYINwMK6MnqwC5m+rg4a6SFUNi+nc864YCUc/rV9e80sD5AYA2tvBl+BfrX5/LZvTXiiEpvBwpX5yNzQuNZuO/Yq4CbTIqgpNiIiIgqQ5M1wb+pRtVVNMNQJkTRZHhnsdSfYAIkMkqpE74/Ni2tY0lDOL9KV2VTOsxkkzvg/eM71j/DpO3axuK6cg+39PPbCHvc5qUts9rX2seNkD2+abHNWT6YeJLn0HwGoXZIoZ5hKD5KRfhjstMGbHDNI5lWWMOiz19Dfc455ppdIeZE2aPWUVEIsbL83mi+c6auZeSUV2fUg0R1/yYU36vfMC4CjAIkUnN9vVGIjIiJzngIk+TLckzpAUu0GNAYmV2YTjsY41N5vG7RCygwSYwyv2LyQ3xw8S89gePxJKubbviUpFnN/fccu+ocjfOPdl/LGS1o4cOQoMX9Z2lKXHz5zgqDf8NqLpxhASJtBkqY0aTJ8Ppi/xr496QySKptB0uuO+M2xB4kxhkBlPQBDboDEqSj2AIkbfFp1Q+EyM4pJSVWWJTbqQSI5qF5os+tOP2vfV4BECizo8xFWgEREROY4rXbyJVOJDUy6zObo2QFC0VhSBkkb+EugrG7Uca/YspBIzOGe3a3jT+I1rBwzyeZXO8/wix1n+PgNq1nbVM1fvXIjiwN9dMRqCKVIrw1HY/z0+VNcv34BDZUlk/o44jL1IMk1gwQSfUhKJ9uk1Z1i03PKvl+TW4kNQFm1/byP9J1lHr34qot0go3HC5rN9v4j0yVYkWWJjQIkkgNjbPngKQVIZHr4NeZXREREAZK8iMVsL410TVohXiITy/LuTHyCjRcg6WuzwZbkMcLABS21LK4r55c7UwRIvH4iA4k+JF0DIf7ypzvZtKiGD750FQC1FUEua4pxJlrFFx88OO40D+3r4Gx/iDddsiSra08pUw+SyTZWTcWr15/suUqr7DX0ugGSHDNIACpr3cBU11GCJkqwZpIjkWebkkrAwKrrZ/pKZoeSiswZJNGInaikAInkqnGdbZAM+n6SgvNrzK+IiIgCJHkx0gs4E2aQdPSNcMHf3sN96ZqqJtnX2offZ1i9wL17f/q5RBlJEjvNppmH93fwk+dOjn7QK+1IatT6t3fuonswzD+/8UKC/sSXfz49BKsX8MUHD7L7dO+o09y+/QTzq0p46br0I4AnVMgeJAAbXgObXgd1yyb3PG+Ebc9J8AWhMvdsj7oG+3kKdh8CoKy2yAMkG34brvp929NGbG+RTGN+vcdUYiO5mr828XYuk75EshDUmF8REREFSPLCG1WbqgeJ1/S0v50dJ7vpH4nwrcePTnjKva19rJhfSWnAD93H4ey+tCUOH752NVuX1fOH33+BT/14B8PhqPvao0ts7tvdxk+fP81Hr1vNxkVjgjkDHaxeuZK6ihI+cfsLhKM2zfZc/wj372nndRcvHhVQmbRMGSS59iABm0Hypm9CYJIlQCVVtgFp1xGoWZiXHhvzGuYTcXyUdh8GoKyuOedzzqiNr4Eb/3amr2L2mKhJqxcg0R1/yZXXqBVUYiMFZ8f8KkAiIiJzmwIk+TDsBUhSLPQDpVBeD/1t8bKZRw+e5VT30Phjk+xt7U00aD14n92mCZA0VJbwnfddzkeuXcV3nzrB6/7rMY6cHUj0IBmwTVz//Ccvsr65mo9et3r0CRwHBjoorW3i/712E7tO9/LlX9vsh589f5pIzOGNuZTXwAQ9SPIQIJkqL3ulY19e+o8ALKovp5cKGoZtary/2HuQyGjBiswZJF7wRAtayVVjcgaJAm5SWEG/IRJVDxIREZnbFCDJBy+DJN1Cv6oZ+lrZ19pHdVkAx4GfPHsy9bFA/0iEk11DSQGS+6F26eh06zECfh9/est6vvG7l3KmZ4jf/sKj/GLfgC0bGTzL3/1iN+cGQvzLmy6kJDDmyz7cDbEIVDZyy+aFvPKChfzH/QfZ39bHD7ef5IKW2kQvlKnyB8H4RwdIohG70JzJAIm3iD13KC/9RwAW1ZXT41RS67jfF2lGJ0uR8sqy0vG+x72goMhU1S1LZN+pxEYKTBkkIiIiCpDkx3CP3aYrFalaAP3t7Gvt49LlDVyxsoHbt5/EcVL/IbIv3qC1BiIhOPwQrL5hXIPWVK5bv4BffPwa1jRV8dHvPkevv5aTp05y+/aTfOilK9m8OEUZUH+H3br9Nz7z6k1UlQV477eeZs+ZXt54SX4yKwiWjy6xiQeW8tCDZKq8RUcsDDX5CZAsrC2jBxt4ieKDioa8nFdmiQkDJF6JjTJIJEc+P8xze0+pZEsKLOBTDxIREREFSPIhXmJTl/rxqiac/jYOdfSzrrmaN16yhKPnBnnmWFfKw70AyfrmajjxJIT6JzVidXFdOd//wJW87+oVnBqpYM+hI6xeUMXHbxjf5BWAAS9AYpu6zqsq5W9fvYkTnUOU+H28+sJFWb92RoGy0RkkI/bjzEsPkqlKvitbm59AUHVZkAGfDfr0+2rsIkfOH1mX2GhBK3nQ6I4wV8mWFFhAJTYiIiIKkOTFhCU2C3D624jEYqxvruYVW5qpLPHzw2dOpDx8X2svlSV+FteVw8F7wReAlS+d1CWVBHz85as20ti8mCWlA/zbmy+0DV9TiQdIEqUgr7pgIe+4fCnvvno5dRWTbHyaTrBi9mWQlCYFSPKUQQIQDtrvhYFgfd7OKbNESaUd4xuNpH48nkGiEhvJg5ZLbR8rBUikwAIqsRERESEw0xdwXpiwxKYJX2SYKoZY11xNRUmAV2xZyC92nOFvXr2JipLRX4a9rX2sba7G5zO2/8jSK6ccRJi/YBHzI23QUpf+oBQBEmMMn33dlim9ZlrBNBkkM9qDJDmDJH8BklhpLYRhuETlNecdr9QhPAD+FCVr8R4kWtBKHlz2frjwLcpEk4Lz+wyRqAIkIiIytymDJB+Ge8BfaifWpFLVBECzr5eV8+2C/E3bljAQivLLF1tHHeo4Dvva+mx5Te9paNs5qfKacSrmwcC5zMcMdAAmMfWmUAJlozNIhifIvJkOyXdl8zTFBsBU2MyRUOn8vJ1TZgmvdCaUpsxGJTaSTz6/zSARKbCA30ckphIbERGZ2xQgyYeRXihLcSfZU20DJBfVD8cnyFy6vJ5l8yq4ffvoaTbtfSN0D4ZZ11Rts0cgxwDJfBjpsc1e0xnosI1E/QVOKAqWz74eJF5mTqAsr81USyrtuWKFDjrJ9PMyQ9L1IYmX2ChAIiLFI+AzatIqIiJzngIk+TDcm3mR72aQbK5NZE8YY3jj1hYeP3yOE52Jhdbe5Ak2B++F6oXQtGnq11bpLtCHOtMfM9AxPaNox2aQjLilSbNhik3N4qymBGWrrMb9vGvE7/nHyzoK9ad+3MsgUYBERIpIwOcjrBIbERGZ4xQgyYeR3oxlIr0Bm02wumL0aNDXX9KCMfCjZxNZJPtabdnJ+gXlcOihrMf7plXhlngMnE1/TP80BUjSZZDMZIlNoBSMP6/9RwDqGuzns6SmKa/nlVlgohKb8BAYX/qSOxGRWUgZJCIiIgqQ5MdwT8YMkn09fkKOn5Zg36j9i+vKuWrVfG7ffpKY+0fJ3tY+FlSXUt+1w2ZYrL4xt2tzR/fSk3piDjBzGSTDvTY4MZPTPoyxGSy1S/J62pVLbT+TVStW5PW8MgvES2wGUj8eHrTH5DEjSUSk0Px+ox4kIiIy5ylAkg/DmXuQ7G0b4Cy1NJqecY+9aVsLJ7uGePKILYHZ19rHuuZqOHCvDR6svDa3a1t0MVQ1wyP/Ck6aO0MDZ6cxgyS5xKbPBpZmeiH525+Hl/xeXk9pll4JN/0/zKrr83pemQWyadKqBq0iUmSCGvMrIiKiAEleTFBis6+1l05TT0Vo/DSZmzY2U10a4IfbTxCJxjjQ3s+GhTVw8D5YchmU1+V2bSWVcMNfwcmnYeePxj8eHraZKlXTlUGSXGLTO7P9RzybXgsLNuT3nP6gDboEy/J7Xpl58TG/GZq0zmRWlIjIFPh9PqLqQSIiInOcAiT5MEEGyb7WPoZK5mP62sY9Vl7i51UXLuKXL7ay83QvoUiMC+pG4Mzztv9IPlz4dmi+AO799OgeIACDbm+SmcogKc0w/UdkNoo3aU1XYjOUKMMRESkSQb8hrBIbERGZ4xQgyVU0bHsRpMkgcRzHTqapWgD94wMkAG+8pIWhcJR/v3c/ABeFnrUP5Np/xOPzwc1/D70n4fH/HP3YQIfdTlsPkqQAzfAsySARmYyJMkhUYiMiRcivJq0iIiIKkOTMm8SSJoPkTM8wfcMRSusW2myNWHTcMVuX1rGysZJf7+/A7zMs7HjUBiyaL8jfda64Bta/Ch75d+hrTewfmOYMklgEohH7/sgE45FFZqMJM0gGNeJXRIpOQD1IREREFCDJ2bDbeDXNQn9fqw2g1DS2gBNLOW7XGMMbL7FTT1Y2lOI//CCsfpnN/MinGz8D0RA88HeJff3tdjtdGSSQyCKZLT1IRCbD57ffy+kCJCEFSESk+AT8PhwHZZGIiMicpgBJrrwASZoSm71ugKRx4VK7I02ZzesvbsFn4Kb6MzDUaQMk+TZvFVz+QXju/+DMC3bfdJbYeI0rvT4kI30Zm9uKzFrBisxNWlViIyJFxu+zE+U06ldEROYyBUhyNdJrt2kzSHpZWFtGZcMiu8PL2BijubaMf37jhdzaeBAwUKjxsL/1CSivh7v/wo79HeiAQHmibKCQvAyS8KB9bfUgkWJVUpl+zK9KbESkCAX9boBEk2xERGQOU4AkV8NegCR1D5K9rX2sa66G6ia7o7815XEAb7ikhab2R2HxJVDRkO8rtcrr4Lo/h6OPwN5f2ABJVSMYU5jXS+ZlkESGITICsbB6kEhxClbY5sypqMRGRIqQ3y3rVR8SERGZy7IKkBhjfmyMeaUxZlIBFWPMLcaYfcaYg8aYT6Z4/N+NMc+7//YbY7qTHnuXMeaA++9dSfsvMca86J7zP4yZjpV9Bl4GSYpSkXA0xqGOfhsgqVxgd6YpsQFgsBNOPgNr8jS9Jp1L3g3z18G9fwW9p6envAaSSmyGMn7eRGa9korMGSQqsRGRIhNwS2zUg0REROaybAMe/wW8HThgjPmcMWbdRE8wxviBLwIvBzYCbzPGbEw+xnGcP3Qc5yLHcS4CvgD82H1uA/Bp4HLgMuDTxph692lfAt4PrHH/3ZLlx1AY8Sat4zNIDncMEI46rG+utgum0pq0JTYAHHoAcArTfySZPwA3fxY6D9tMkukKkMSbtA4npv8oQCLFKFiZugdJNGwzo4LTULImIpJHgXiJjXqQiIjI3JVVgMRxnPscx3kHsBU4CtxnjHnMGPNuY0wwzdMuAw46jnPYcZwQ8D3gNRle5m3Ad923bwbudRyn03GcLuBe4BZjzEKgxnGcJxzHcYDbgNdm8zEUjFdik6KXxt5W+9i6JjcIULUgcwbJwfugvAEWXZzvqxxv9csSfU5mIoMk3txWPUikCJVUQqh//H5vso0ySESkyATiTVqVQSIiInNX1iUzxph5wO8C7wOeAz6PDZjcm+Ypi4ETSe+fdPelOvcyYAXwwATPXey+PeE5p81Ir71b7B8fJ9rX2kfAZ1i1wL2bXNWUPoPkyMOw505YfYMdI1poxsDNfw/GD7UthX89SJ1Boh4kUozSldh4WSVeMFBEpEjEe5CoSauIiMxhgWwOMsb8BFgH/C/w247jnHEf+r4x5pk8XMdbgdsdx4nm4VwAGGM+AHwAYOnSpfk67XjDPRkm2PSxsrGS0oAb8KhaAK0vjj9wxw/hpx+2Y3hf9reFu9axFmyAD/4a6gr4+UmWnEESi9i3lUEixShdiU14KPG4iEgRiU+x0ZhfERGZw7LNIPkPx3E2Oo7zD0nBEQAcx9mW5jmngCVJ77e4+1J5K4nymkzPPeW+PeE5Hcf5iuM42xzH2dbYWMASkuGetH007ASbpMeqmqEvqcTGceDR/w9+/D5Ycjm851dQO80JMc1b0k7gyTv1IJHzRUlFopwmmUpsRKRI+dWkVUREJOsAyUZjTJ33jjGm3hjzkQme8zSwxhizwhhTgg2C3DH2IGPMeqAeeDxp993ATe7r1AM3AXe7wZleY8wV7vSaW4GfZfkxFMZIb8oMkr7hMKe6h2yDVk/VAgj12UVULAp3fQLu+zRsfgP8zo+hvH7cec4ro3qQaIqNFLFgRZoMksHE4yIiRSTgltiEVWIjIiJzWLYBkvc7jtPtveM2Tn1/pic4jhMBPoYNduwBfuA4zi5jzGeMMa9OOvStwPfcpqveczuBv8MGWZ4GPuPuA/gI8D/AQeAQ8MssP4bCGO5NmYGxv81mSKxrSg6QNNlt1zH4/u/A01+Fl3wcXv8/ECidjqudWSkzSFRiI0WopBKiIYhGRu/3MkgUIBE57xljvm6MaTfG7EzzuDHG/Icx5qAxZocxZmvSY1FjzPPuv3E3j2aCxvyKiIhk2YME8BtjjBfEcEf4lkz0JMdx7gLuGrPvr8e8/zdpnvt14Osp9j8DbM7yugtvpBfql4/bvbfVDZA0pwiQ/N8bofc0vPyf4PIPTsNFzhLJGSQjPTZgEpjw20hk9vECIOEB8CcFSL0eJCqxEZkLvgn8J3aiXiovB9a4/y4HvuRuAYYcx7mowNc3KX63B0lYPUhERGQOyzaD5FfYhqw3GGNuwPYL+VXhLquIpGnSuq+1j6rSAC31SdMsqhbY7eA5ePNtcys4AuAvAUwig0TlNVKsStwmrGP7kMRLbNSkVeR85zjOw0BnhkNeA9zmWE8AdcaYhdNzdZMXdEtslEEiIiJzWbYZJH8GfBD4sPv+vdgyFxnuTbnQ39vax9qmKmyrFFfjOrjonbD1Vlh6+bjnnPeMsVkkXg8SlddIsYoHSMb0IVGTVhFJWAycSHr/pLvvDFDmTgGMAJ9zHOen0395o3lNWjXmV0RE5rKsAiSO48SwqaFfKuzlFJnICERHxvUgcRyHfa19vGLLmBtFgVJ47Ren8QJnoUBZIoMkzXhkkVkvucQmWTyDpBwRkQyWOY5zyhizEnjAGPOi4ziHxh5kjPkA8AGApUuXFvSCNOZXREQkyxIbY8waY8ztxpjdxpjD3r9CX9ys501iGRMgaesdoWcoPHqCjVjBcggP294tyiCRYuVliIzNIFGJjYgknAKWJL3f4u7DcRxvexh4CLg41Qkcx/mK4zjbHMfZ1tjYWNCLjWeQqMRGRETmsGx7kHwDmz0SAa7DNiT7dqEuqmgM99jtmBKbva02cLJOAZLxAmUQGVIPEiluXgBkbAZJaBB8ATUfFikyxpjfN8bUuJNnvmaMedYYc1OOp70DuNU95xVAj+M4Z4wx9caYUvd15wNXAbtzfK2ceWN+oyqxERGROSzbAEm54zj3A8ZxnGPu5JlXFu6yisSIGyApGxsgsRNslEGSgpdBkqZ3i0hRyJRBohG/IsXoPY7j9AI3AfXA7wCfy/QEY8x3gceBdcaYk8aY9xpjPmSM+ZB7yF3AYeAg8FXgI+7+DcAzxpgXgAexPUhmPkCiEhsREZGsm7SOGGN8wAFjzMewKaJVhbusIpGmxGZfax9NNaXUVegu8jiBMruIVA8SKWbxHiQpmrQqQCJSjLyO6q8A/tdxnF1mVJf18RzHedsEjzvAR1PsfwzYMtULLZSASmxERESyziD5faAC+DhwCfBO4F2FuqiikbbEpo91zVr8p+RNsVEPEilmJW58ONQ/en94SBNsRIrTdmPMPdgAyd3GmGpgTqVSaIqNiIhIFhkkxhg/8BbHcf4E6AfeXfCrKhYjXgZJIhgSjsY41N7PNWvmz9BFzXLBcug7AzgqsZHilbHERg1aRYrQe4GLgMOO4wwaYxqYY3/vBP32npkySEREZC6bMIPEcZwocPU0XEvx8Upskhb6R88OEIrG1H8knUAZ9HfYt5VBIsUqY4mNRvyKFKErgX2O43QbY94J/CXQM8PXNK28DJKoepCIiMgclm0PkueMMXcAPwTiYxscx/lxQa6qWIz0AmZUgORAu025X9ukxX9KwfK0zW1FiobPb4N9oTFTbMKDUKIMEpEi9CXgQmPMhcAfA/+Dndj30hm9qmnkNWkNq8RGRETmsGwDJGXAOeD6pH0OMLcDJMM9NgvCl0jE6R0KA9BQqQatKQXKEm+rxEaKWbBifAZJeAgqF8zM9YhILiKO4zjGmNcA/+k4zteMMe+d6YuaTvExvyqxERGROSyrAInjOHOqDjdrKUbVjkRsampJINv+t3NMcvmBAiRSzEoqx/cgCQ2oSatIceozxnwKO973GndyX3CGr2laeSU24ahKbEREZO7KKkBijPkGNmNkFMdx3pP3KyomI73jykRCCpBkNiqDRGVIUsSCFSmm2AyqB4lIcXoL8HbgPY7jtBpjlgL/PMPXNK2Cfq8HiTJIRERk7sq2xObnSW+XAa8DTuf/corMcA+U1Y7aFXLvvJT4FSBJaVQGiQIkUsRKKlOX2GiKjUjRcYMi/wdcaox5FfCU4zi3zfR1Taf4mF8FSEREZA7LtsTmR8nvG2O+CzxakCsqJsM9UL1w1K54iY0CJKklZ5CoSasUs7ElNo6jEhuRImWMeTM2Y+QhwABfMMZ8wnGc22f0wqZR0O1BElGTVhERmcOyzSAZaw2gToQjvdC4btSuUCRG0G/wuXdiZIzkDJKSqpm7DpFcBSugvzXxfjQETjQxAlhEislfAJc6jtMOYIxpBO4D5kyAxOczGKMxvyIiMrdl24Okj9E9SFqBPyvIFRWTFE1aQ5EYpQH/DF1QEfAySEqq7ahUkWJVUjE6g8Qb+asAiUgx8nnBEdc5YM6lggZ8RiU2IiIyp2VbYqNmEWM5jtukdWwPkqgatGbiZZCo/4gUu+CYHiThIbtViY1IMfqVMeZu4Lvu+28B7prB65kRAZ9PARIREZnTslrJG2NeZ4ypTXq/zhjz2oJdVTEID0IsknKKjfqPZOBlkKj/iBS7kopE1ggkgiVq0ipSdBzH+QTwFeAC999XHMeZc5myAZ9RDxIREZnTsu1B8mnHcX7iveM4Trcx5tPATwtyVcVguNdux5TYjERiyiDJRBkkcr4IjgmQxEtsNOZXpBi5Del/NOGB57GA3xBRDxIREZnDsg2QpFrxT7XB6/lhxA2QjC2xUYAkMy+DpFQZJFLkSqogFoZoGPzBRAaJSmxEikaKHmvxhwDHcZw59Z+VXyU2IiIyx2Ub5HjGGPNvwBfd9z8KbC/MJRWJ4QwBEpXYpKcMEjlfeIGQ0ACU16nERqQIqcfaaAGfIaoSGxERmcOyXcn/HhACvg98DxjGBknmruEeux07xSaqDJKM1INEzhfetBovMBJSBomIFLeA3xBWiY2IiMxh2U6xGQA+WeBrKS4jboCkTD1IJiWeQaIAiRS5EjdTxAuMxDNIFCARkeIU8BmiKrEREZE5LNspNvcaY+qS3q93x+HNXWmatIYiMUoVIElPARI5X8QzSNzmrPEmrQqQiEhx8muKjYiIzHHZruTnO47T7b3jOE4XsKAgV1QsvBKbFD1IFCDJoLTGltnULJrpKxHJTbwHiZdBMjR6v4hIkQn6fZpiIyIic1q2TVpjxpiljuMcBzDGLCd11/e5Y6QXjD+RZu9SD5IJlFbBR59SgESKX0mV3XqZIyqxEZEi51eJjYiIzHHZBkj+AnjUGPNr7Oi7a4APFOyqisFwr53EYsyo3Zpik4X6ZTN9BSK5S1Vi4wvakb8iIkUo4PcRVomNiIjMYdk2af2VMWYbNijyHPBTYKiA1zX7jfSmnMQyEokqg0RkLhhXYjOo8hoRKWpq0ioiInNdVgESY8z7gN8HWoDngSuAx4HrC3Zls91wz7j+I+BmkChAInL+C7rldeGkEptgZfrjRURmOb/PqAeJiIjMadmu5H8fuBQ45jjOdcDFQHehLqooDPdCaZoAid8/AxckItNqbAZJSBkkIlLcgn5NsRERkbkt2wDJsOM4wwDGmFLHcfYC6wp3WUUgTYmNmrSKzBHxHiRJJTbeGGsRkSLk9/mIqMRGRETmsGybtJ40xtRhe4/ca4zpAo4V6qKKwnCvHVmbJBZzCEcdBUhE5gKf346sDiU1aVWJjYgUsaBKbEREZI7Ltknr69w3/8YY8yBQC/yqYFdVDFL0IAlF7R8VpQqQiMwNJZVJY36HUmaViYgUC79PJTYiIjK3ZZtBEuc4zq8LcSFFJRZLWWKjAInIHBOsHF1iU908s9cjIpKDgF9TbEREZG7TSn4qQv2AM67EJhSxARKV2IjMESUVY0ps1KRVRIpXQD1IRERkjtNKfipGeu12bAaJFyDx69MqMicEK5IySIY0xUZEilpAPUhERGSO00p+KoZ77HZMD5IRZZCIzC0llYkxv+FBNWkVkaKmHiQiIjLXaSU/FcNuBolKbETmtmAFhAfAcdwSG435FZHiFfCrxEZEROY2reSnIl5iM2aKjUpsROaWkgqbQRIZBhyV2IhIUQv41KRVRETmNq3kpyJdBkk0CiiDRGTO8Mb8hofs+yqxEZEiFvAbwlH1IBERkblLK/mpGO622zQ9SEoD/mm+IBGZEcFKW2LjTbJRBomIFDFlkIiIyFynAMlUTDTFRhkkInODV2LjTbLRmF8RKWJ+jfkVEZE5Tiv5qRjuBV8QAmWjdofiGST6tIrMCcFKiIUTk60UIBGRIhb0GyIqsRERkTlMK/mpGO6x2SPGjNodiiqDRGRO8UpqBs6Ofl9EpAj5fYaYAzFlkYiIyBwVmOkLKEqb3wCLLxm3W1NsROYYL2Nk0A2QqEmriBSxoPv3SyTmUOIzExwtIiJy/lGAZCpWXGP/jTGiHiQic0uJGxDpb7fbYPnMXYuISI78blBEjVpFRGSu0ko+j9SkVWSO8QIkKrERkfNAwA2QRGLqQyIiInOTVvJ5pACJyBzjldgMdLjvq8RGRIpXPEASVQaJiIjMTVrJ51G8Sat6kIjMDV4GSbwHiUpsRKR4+ZN6kIiIiMxFWsnn0YjG/IrMLcGxU2yUQSIixUslNiIiMtdpJZ9HoUiMEr8PY9T5XWROKEkqsfGXgs8/s9cjIpIDldiIiMhcpwBJHoUiMfUfEZlLgklNWtWgVUSKXMCvKTYiIjK3aTWfR6FoVAESkbnEC4o40US5jYhIkQr4vB4kKrEREZG5Sav5PBoJx9SgVWQuSQ6KKEAiIkUu0YNEGSQiIjI3aTWfR6GoSmxE5hSfHwLu5BqV2IhIkfOrB4mIiMxxWs3nkXqQiMxBXmAkqAk2IlLcghrzKyIic5xW83nkTbERkTnEC4wEy2f2OkREcuRlkETVg0REROYorebzSCU2InOQl0GiEhsRKXLeFJuwSmxERGSOKuhq3hhzizFmnzHmoDHmk2mOebMxZrcxZpcx5jtJ+//RGLPT/feWpP3fNMYcMcY87/67qJAfw2SMRGKUKkAiMrcEVWIjIucHb4qNxvyKiMhcFSjUiY0xfuCLwI3ASeBpY8wdjuPsTjpmDfAp4CrHcbqMMQvc/a8EtgIXAaXAQ8aYXzqO0+s+9ROO49xeqGufqlAkRnVZwT6lIjIblbiBEWWQiEiR82uKjYiIzHGFTHe4DDjoOM5hx3FCwPeA14w55v3AFx3H6QJwHKfd3b8ReNhxnIjjOAPADuCWAl5rXoSUQSIy93gBEo35FZEiF/R7U2zUg0REROamQq7mFwMnkt4/6e5LthZYa4z5jTHmCWOMFwR5AbjFGFNhjJkPXAcsSXreZ40xO4wx/26MKS3UBzBZ6kEiMgfFS2wUIBGR4qYMEhERmetmejUfANYA1wJvA75qjKlzHOce4C7gMeC7wONA1H3Op4D1wKVAA/BnqU5sjPmAMeYZY8wzHR0dBf0gPCORqKbYiMw1atIqIueJ+Jjf2dak1XHgxdshNDjTVyIiIue5Qq7mTzE666PF3ZfsJHCH4zhhx3GOAPuxARMcx/ms4zgXOY5zI2Dcx3Ac54xjjQDfwJbyjOM4zlccx9nmOM62xsbGvH5g6YQiyiARmXOCKrERkfNDIoNklpXYdB6GH70X9tw501ciIiLnuUKu5p8G1hhjVhhjSoC3AneMOean2OwR3FKatcBhY4zfGDPP3X8BcAFwj/v+QndrgNcCOwv4MUyKAiQic1CJSmxE5PwQcAMks26KzXC33Q51zehliIjI+a9gI1ccx4kYYz4G3A34ga87jrPLGPMZ4BnHce5wH7vJGLMbW0LzCcdxzhljyoBHbAyEXuCdjuNE3FP/nzGmEZtV8jzwoUJ9DJMVisQo8ftn+jJEZDoFNcVGRM4PgdlaYuOV1oz0Zj5OREQkRwWdSes4zl3YXiLJ+/466W0H+CP3X/Ixw9hJNqnOeX3+rzQ/QtEYpUFlkIjMKfEpNpUzex0iIjkKzNYmraEBux3umdnrEBGR855W83kSizmEo46atIrMNfESm/KZvQ4RkRz54yU2s6wHSajfbpVBIiIiBabVfJ6EovaPCfUgEZlj4iU2yiARkSKy/264569G7Qr67N8w4dlWYhN2S2yGFSAREZHC0mo+T7wASakCJCJzy8ILoHEDNKyc6SsREcneyafhsS9AUraI3z9Lm7R6JTbKIBERkQIraA+SuSQUUQaJyJzUuA4++sRMX4WIyOSU1QIOhPrctxM9SMKztcRGPUhERKTAtJrPkxEvQKIeJCIiIjLbuUGR5KBDfMzvbCuxiTdpVQaJiIgUllbzeaIMEhERESka8QBJIujgn7VTbDTmV0REpodW83miAImIiIgUjRQZJMYYAj5DZNaV2CiDREREpodW83kSUomNiIiIFIvSGrsd09fD7zOzMIPE7UESGYJoeGavRUREzmtazedJKBoFoDTon+ErEREREZlAigwSgKDfR2S29SDxxvyCskhERKSgFCDJEzVpFRERkaJRVme3KTJIZu2YX4ARTbIREZHC0Wo+T9SDRERERIpGmVtiM6bx6ezsQdKfeFsZJCIiUkBazeeJFyApVYBEREREZjt/EIKV4zJIAn4z+0psQgNQ2Wjf1iQbEREpIK3m82REGSQiIiJSTMpqYbh71K6AzzcLm7QOQvVC+7YySEREpIC0ms8TTbERERGRolJWUzw9SLwAiTJIRESkgLSaz5NQVBkkIiIiUkTKalOW2ISjs6gHiePYHiQ1yiAREZHC02o+T9SkVURERIpKqgDJbMsgiYyAE1UGiYiITAut5vNETVpFRESkqJTVjsvICPh8hGdTk9bwoN2W1UGgfFxAR0REJJ+0ms8TldiIiIhIUUlTYhOdaMzv3l/AUFcBLyyJN+K3pNL2TFEGiYiIFJBW83kyoiatIiIiUky8AImTyBjx+0zmKTYD5+B7b4fnvzMNF4ht0Ao2QFJaox4kIiJSUFrN50koEqPE78MYM9OXIiIiIjKx0hrb38MLQgBBn49IphKbwXN2O3C2wBfnCrklNsogERGRaaAASZ6EIjGV14iIiEjxKKu126QymwnH/A51jt4W2qgSm/E9U0RERPJJK/o8GYlEFSARERGR4pEiQBLwG8KZepB4vUemrQfJmBIbZZCIiEgBaUWfJ16JjYiIiEhR8AIkSUGHCcf8zliApMqW2CiDRERECkgr+jwJRVViIyIiIkWkrM5uR5XYTNSDxC2tGZymAEnYDZAEK5RBIiIiBacVfZ6oB4mIiIgUlRQlNkG/ITJbS2zKaiE8CNHw9Ly2iIjMOVrR50koEqNUARIREREpFmU1djumSWvGMb8z3YMEVGYjIiIFoxV9nqjERkRERIpKPODQHd8V9Puym2ITHoDISOGuzRMaAH8J+IOJgM5IT+bniIiITJFW9HkyoiatIiIiUkyCZRAoG59BkqkHSXLmyHRkkYQGbPYIKINEREQKTiv6PFEPEhERESk6ZbWjAg4BX5Y9SMa+XSihATvBBpIySBQgERGRwtCKPk/Ug0RERESKTlntqAySgH+CDJLBLqhe6L7dWeCLA0L9yiAREZFpoxV9noxEosogERERkeJSWjM6QOLzTdyktWFl4u1CCw/aEb+gDBIRESk4rejzJBRVDxIREREpMmMySPw+k75JazQMob7pDZCM6kHijSVWgERERApDK/o8UQ8SERERKTopSmzC0TQ9SLyAyLxV7vvTVWKjHiQiIjI9tKLPEwVIREREpOiU1Y4KOAQyZZB4AZLaJeALTlMGySCUuCU2/iAEykcFdERERPJJK/o8sU1a/TN9GSIiIiLZ8zJIHBsU8XqQOE6KIIkXECmvt/+mu8QGbBaJMkhERKRAFCDJk1BUGSQiIiJSZMpqIRqCyDBgM0iA1Fkk3tSa8nqoaJimKTZJY37BbSqrAImIiBSGVvR5EIs5hKOOmrSKiIhIcfH6erhlK36/DZCknGQz3RkkjjN6zC8og0RERApKK/o8CLnNzJRBIiIiIkWlrM5u3QBJ0Gf/lskYIKlocAMk3YW9tsgw4CTG/IIySEREpKC0os8DL0BSqgCJiIiIFJMyb3Sum0HildhEUwVIOsH4bZCivKHwU2xCA3abXGKjDBIRESkgrejzYCSsDBIREREpQvEAiQ06BOMlNilG/Q51QXkdGGO3hS6xCfXbbXKJjTJIRESkgLSiz4N4iY16kIiIiEgxiQdIugHwT1RiU95g3y6vh/AghIcLd23xDJLkHiS1yiAREZGC0Yo+D0IRZZCIiIhIERpTYuNNsUkZIBnstIERsH1IoLBZJKFBux2bQRIehGi4cK8rIiJzllb0eeAFSEoD/hm+EhEREZkOxpivG2PajTE70zxujDH/YYw5aIzZYYzZmvTYu4wxB9x/75q+q06hdPQUm4BXYhNNV2LjBki8bUEDJClKbLypOyN9hXtdERGZsxQgyQNlkIiIiMw53wRuyfD4y4E17r8PAF8CMMY0AJ8GLgcuAz5tjKkv6JVmEiwHX3Bck9bUJTbdicyRaQmQpCmxgfj1ioiI5JNW9HkQikYBBUhERETmCsdxHgYyjXF5DXCbYz0B1BljFgI3A/c6jtPpOE4XcC+ZAy2FZYwNOsRLbOzfMtGUAZKkEhuvF0khJ9mE3RKb4JgSG1AfEhERKQit6PNgJKImrSIiIjLKYuBE0vsn3X3p9s+cpManXolNeGyJTSRkS15mS4mNJtmIiEgBaEWfByqxERERkXwzxnzAGPOMMeaZjo6Owr3QqAwSGyAZl0HiBUKmNUCSosRGGSQiIlJAWtHnwUi8Sas+nSIiIgLAKWBJ0vst7r50+8dxHOcrjuNscxxnW2NjY8EudFSAxM2GDUcnCJCUVIK/xE62KZSUPUhGN5UVERHJJ63o80AZJCIiIjLGHcCt7jSbK4Aex3HOAHcDNxlj6t3mrDe5+2ZOWU32GSRek1ZjbLCk0BkkgTLwJU0JLPWatCqDRERE8i8w0xdwPgipB4mIiMicYoz5LnAtMN8YcxI7mSYI4DjOfwN3Aa8ADgKDwLvdxzqNMX8HPO2e6jOO4xQwDSMLSRkkiSk2Y3qQeM1Yy5MG7kxHgCQ5ewSSxvwqQCIiIvmnAEkehKLKIBEREZlLHMd52wSPO8BH0zz2deDrhbiuKSmrjWdkBN0mrZGJSmzATrKZ7gCJPwiBcpXYiIhIQWhFnwch9SARERGRYlVWC5EhiIzgTzfmNx4gaUjsK3QGSXhg9IhfT1mNMkhERKQgtKLPA/UgERERkaJVVme3w73xHiSRsQGSwU4wfiitTuybiRIbsJNs1INEREQKQCv6PFCJjYiIiBStMq/xaQ+BeInN2B4kXTYgYkxiX0V94afYpAqQKINEREQKRCv6PBhRk1YREREpVqWJ0blpM0iGuhITbDzl9bY0JzxUmOsKDUBJ1fj9yiAREZEC0Yo+D0KRGCV+Hyb5roqIiIhIMYhnkHQTcHuQpJxik9ygFRLvD3UX5rpCA1BSMX6/MkhERKRAFCDJg5FIVOU1IiIiUpy8AMlIb2LMb6opNuMCJG5GyVCBymzUg0RERKaZVvV5EIrEFCARERGR4pSiB8n4KTbdoyfYQFIGSYEataYrsSmrVQaJiIgUhFb1eeCV2IiIiIgUneQAiVtiE041xSZtiU0BAiSxmB3zmy6DJDwI0XD+X1dEROY0rerzIBSNURrUp1JERESKUEmlHeGb1KQ1mjzFJjJigxVjAyRe09ZCTLKJuI1fg2l6kACM9OX/dUVEZE4r6KreGHOLMWafMeagMeaTaY55szFmtzFmlzHmO0n7/9EYs9P995ak/SuMMU+65/y+MaakkB9DNpRBIiIiIkXLGBt0SB7zm5xB4jVhrZjGDJLQgN2myyABGO7J/+uKiMicVrBVvTHGD3wReDmwEXibMWbjmGPWAJ8CrnIcZxPwB+7+VwJbgYuAy4E/Mca4/xvyj8C/O46zGugC3luojyFb6kEiIiIiRa2sdlSJzegAiZshMjaDJFgB/pICBUj67TZlDxIvg0R9SEREJL8Kuaq/DDjoOM5hx3FCwPeA14w55v3AFx3H6QJwHKfd3b8ReNhxnIjjOAPADuAWY+foXg/c7h73LeC1BfwYshKKKkAiIiIiRaysFoYTU2xGNWn1AiBjm7QaY/cVYopNaNBuU435jWeQKEAiIiL5VchV/WLgRNL7J919ydYCa40xvzHGPGGMucXd/wI2IFJhjJkPXAcsAeYB3Y7jRDKcc9qNqMRGREREilk8g8QGSMLJPUjiAZL68c8rr5/+EhtlkIiISIEEZsHrrwGuBVqAh40xWxzHuccYcynwGNABPA5EJ3NiY8wHgA8ALF26NJ/XPE4oEqO6bKY/lSIiIiJTVFYLZw/i8xl8ZkwGyWCaEhtvn9ejJJ8yldgog0RERAqkkGkPp7BZH54Wd1+yk8AdjuOEHcc5AuzHBkxwHOezjuNc5DjOjYBxHzsH1BljAhnOifv8rziOs81xnG2NjY15+6BSGYnEKFWJjYiIiBQrN4MEIOD3jelB4maIVDSMf15FQ2Gm2GTMIKmzW2WQiIhInhVyVf80sMadOlMCvBW4Y8wxP8Vmj+CW0qwFDhtj/MaYee7+C4ALgHscx3GAB4E3us9/F/CzAn4MWQlFoupBIiIiIsWrNClA4jNExpbY+AKpsznK6wpTYhN2e5BkGvOrDBIREcmzgtWFOI4TMcZ8DLgb8ANfdxxnlzHmM8AzjuPc4T52kzFmN7aE5hOO45wzxpQBj9ierPQC70zqO/JnwPeMMf8PeA74WqE+hmyFoupBIiIiIkWsrBbCAxAN4/eZ8VNsyuttU9axCtaDJEOJjT8IgXIY0ZhfERHJr4I2znAc5y7grjH7/jrpbQf4I/df8jHD2Ek2qc55GDshZ9YIRWKUBvwzfRkiIiIiU1NWa7cjfQT9PiLRMSU2YyfYeMobIDIE4SEIlufvejKV2IDNIlEGiYiI5JnSHvIgFNGYXxERESliXoBkuDtFBklX6gatkNif7yySUIYSG7CNWoeVQSIiIvmlVX0eKEAiIiIiRS0eIOkh6DNEY0k9SAZnIkDSb4MjvjR/X5XVqEmriIjknVb1eRCKKkAiIiIiRSwpQOL3m/ElNqkm2EBif74n2YQG0pfXgJtBogCJiIjkl1b1OYrFHMJRR01aRUREpHjFJ8P0EPCNHfPbOQMZJBMESJRBIiIiBaBVfY5C7hg8ZZCIiIhI0YpnkPTaMb9eiU142I7cLa9L/bxCBUjCgxBUBomIiEwvrepzNBKxf0CUKkAiIiIixSq5xMaXVGIz3G23mabYgM0yyadQ/wQZJLXKIBERkbzTqj5HoYgySERERKTIlVQDxpbY+A1Rr8TG6y2SrsQmWA7+0ukvsSmtsVkm0XB+X1dEROY0repzFC+xUQ8SERERKVY+n+3r4fYgCXsBEi/wka5JqzE2eFKIMb8T9SABGOnL7+uKiMicplV9jrwMktKgPpUiIiJSxEpr3QBJ0phfL/CRLoMEbPAk71NsJiixKU00lRUREckXrepzFC+x8ftn+EpEREREclBWGy+xCXs9SIYmKLHxHhvqzu+1ZDPFBtSHRERE8koBkhypB4mIiIicF9zGpwGfL9GDJJ5BkqbEBtwASb4zSLLoQQKaZCMiInmlVX2OQtEooACJiIiIFDk3g8TvM0SSAyS+YOZgRb57kMSiEBnKPOZXGSQiIlIAWtXnaCSiJq0iIiJyHnADJEG/IeI2oWew0wZAjEn/vHwHSMKDdqsMEhERmWZa1edoRCU2IiIicj5IyiAZVWKTboKNp7weIsN28kw+hAbsNmMPklq7VQaJiIjkkVb1OYpPsVGARERERIpZWQ2M9BL0OaNLbDI1aIVEACVfWSTxAElV+mOUQSIiIgWgVX2O1KRVREREzgtuVkYVQ4kSm2wCJN7jeQ+QVKQ/JlACgTIYmaExv4cfgv6OmXltEREpGK3qc6QMEhERETkvuAGSSmdgTAZJFiU2kL9JNtmU2IDNIpmJDJKRfvjf18GT/z39ry0iIgWlVX2OQlFlkIiIiMh5wA2QVDuDRKLJAZK6zM8rn4ESG4iXBE279j3gxKD31PS/toiIFJRW9TkKaYqNiIiInA+8DBL6bQZJeNhOlJnuEpvwLM8gadtpt31npv+1RUSkoLSqz5F6kIiIiMh5wQuQxAaIxmKJgEc2U2zAjgTOBy+DJJihBwnY652JDJK2XXbb1zb9ry0iIgWlVX2OVGIjIiIi5wV3MkxlbMCW2HgBkokySEoqbMPUmSixKVAGycBIhL/86Yt0D4bGPxgPkCiDRETkfKNVfY5GVGIjIiIi5wM3g6TCcUtsvKarEzVpBRtEmShAcvB++M3nJz5XqN9usymxKVAGyZNHzvHtJ45z7+4xWSKO4wZIDAx3Q3ioIK8vIiIzQ6v6HI1EopT4fRhjZvpSRERERKbOzSApjw0QSS6xmSiDxDtmogDJI/8GD/2jDTJkEhoEDATLMx9XVgvDhRnze/zcIAC7To8JwPSctKOFF11s3+9XmY2IyPlEAZIchSIxldeIiIhI8fMHoKSa8mifm0EymQBJQ+YASWgQTj5lG7AOd2c+V2jAZo9MdPOptMY2kY2GJ76+STrRZTNDdp0eE4DxymtWv8xu+1rz/toiUiC9Z2ygdqIgrcxpWtnnSAESEREROW+U1VIeHcBxIDbglthM1KQV7CjgTAGSE09C1O3n0XMy87lC/ROX14DtQQIw0jfxsZN0otNmkOw+3UsslrSYancDJKuut1v1IREpHi/+EO7/Wzi7f6avRGYxrexzFIrEKFWARERERM4HZbWURW3AITbUCf6SiafJgM0yGeykdzjMYCgy/vGjjyTe7jmV+VzhwewCJG5JUCHKbE50DWEMDISiHD03kHigbRfULYX5a+37mmQjUjx63d895w7N7HXIrKaVfY5CUWWQiIiIyHmirIbSqNskdbDLBj6y6bNW0UBsqIurP3c/n/jhjvGPH3nYBhYAeifKIBmA4GQySPLbqNVxHE50DnLpMps5M6oPSdsuaNpss2p8QWWQiBQTL3utUwESSU8r+xyFIjFNsBEREZHzQ1ltPEDiDHZmN8EGOD1Sji86Qmh4kHv3tDEwkpRFMtIHp56FzW+0QYVJltiEozH+6Vd7OdMzZmJMPINkigGSvlZ4/rvjdncPhukfiXD9hgWU+H3s9PqQhIfh7AFo2mSDRtXN6kEiUkyUQSJZ0Mo+R+pBIiIiIueNslpKI25Pj6GurBq0Pne8i688bfuV/OurlhCKxHh4f0figGOPgxOFlS+FmoUTl9h4TVpdTx/p5L8eOsTnfrl3zLXmmEHy2Bfgpx8at1g60WX7j6yYX8na5ip2exkkZ/fZj2PBRvt+dTP0K0AiUjSUQSJZ0Mo+RyqxERERkfNGWS0lboDEDHVOGCDZfqyL3/naU0TK7HE3ryihviLI3buSAgdHfm17mSy5HGpaEndx0wkNQkmi78lTR23w5Y4XTnOwPakha64ZJEcettvk/ijAcbdB65L6CjYvqmXnqR4cx0lMsGnabLdVTcogESkWkREYcAO3yiCRDLSyz9FIWCU2IiIicp4oqyUY7scQwwx3Q0X6AMn2Y5286+tP0Vhdyh/+9mUABEI93LChifv3thOOxuyBRx6GlssgWA61LdBzIvM1hAagpCr+7jNHu1jaUEF50M/n7z846lqBqWWQDHVB64vu9Y0OkJzotKU8SxrK2bSohq7BMGd6hm2AJFAGDSvtgdULFSARKRZeYLZ+uX07NJi/czsORFM0p5aipJV9jkaUQSIiIiLni7JafMSoZBgznL7E5umjndz6tadYUF3K9z5wBfPmN9sHBju5eVMzfcMRnjh8DgY7bSBixW/Zx2sXQ+8ZiMXSX0NSD5JINMazx7u4dl0jt165nJ/vOM3+NjeLJJcMkmOPAQ7ULbMZJE5ilO+JrkHqK4JUlwXZtNgGYXae6rEBksb14A/YA6ubYbgbwkPjzy8is4tX2rfipXbbdSR/5/7Jh+BrN9o+RVL0tLLPkcb8ioiIyHnDDTosMN34IsMpAyRPHj7Hu77+FM21ZXzvA1fQVFNmp7oADHVxzZr5lAf9tszm2G8AJxEgqVkMsTAMtKe/hqQeJLvP9DIYinLp8gY+8FsrqQj6+fz9B+xxgRKb0TEyhTG/Rx6BQDm85Pegv802X3Wd6BxkSYMt8dnQXIPPuJNsvAk2nmo3KKQsEpHZz8sg8X4X5avMxnHg0P1w+lm479P5OafMKK3scxSKRCkN+Gf6MkRERERy55atLDFurb47xeZQRz9fefgQb/7y47ztq0+wuK6c737gChbUlLnHuYGUoS7Kgn5euraRe3e34Rx5GIIVsPgS+3jtErtNN8kmGoHoSHzM79NHuwC4dHkDDZUl/O5Vy/nFjjPsbXWzRkprppZBcvQRWHIZrLreff/h+EPJAZLyEj+rGqs4ceKoDeo0bUqcwwuQ9LdN/vVFZHp5v3PiAZKD6Y+d1HlP2N4mdUvhyf+Gfb/Mz3llxihAkiM1aRUREZHzRjxAYjM8frRngOv/5SFu+Ndf8/d37aV/OMLHrlvN9z94JQuqyxLPC5bbbI4h21D1pk1NtPWOMLz/IVh6hc32AFtiA+kDJOEBu3UzSJ4+0smShnKaa+1rvf+alVSVBvj8fW7GR1nN5HuQDHZC205YcY3tJ1KzON6HJBpzONU9xJL6RJPYTYtqiJzZad9p2pg4T5WXQXJmcq8vkouuYzDSP9NXUXx6T9mAb9UCqFyQv0k2p7bb7eu/Cs0XwE8/Ar2n83NumRFa2ecoFFGTVhERETlPuAGSZcZmRfxk3xAtDRX83Ws28ZtPXs9dv38Nf3TTOhoqS8Y/t7zBNj8FbljfRJOvh/Lu/Yk7tmCDEZB+kk0oESBxHIdnjnVy6bKG+MN1FSW8+6rl/HJnqx2/O5UMkqOP2u3y3wJjYPk1dp/j0No7TDjqsKShPH745sW1NA66d5tHldgstFuV2Mh0icXgq9fBvX8101dSfHpOJQK081bBucP5Oe+p7XZK16Kt8MZv2Gk5P/4AxKL5Of/5IBqG9r0THzdLaGWfo1BEGSQiIiJynnADJC9ttIGKr3zgRm57z2X8zpXLWVxXnumZtsxmqBuA2oog72x2p9Us/63RxwQrEw0Tx/ImS5RUceTsAGf7Q2xb3jDqkPddvZLq0gCfv3//1DJIjj5iy34WXWzfX3ENDJ6F9j2ccEf8Lm1IZJBsXFTDBt8JRsoaoXJ+4jwVDeALKkAi06f7KAyeg713jWosnLPhXvj1PyUClOejnpN2zDhAw6o8ZpA8azNHAiUwfzW84p/t75hH/i0/5y92jgM/+yh86cqi+V2plX2OFCARERGR80ZZHQBrS84BUFE7P8PBY5TX2/IV180V++h1yjkYWJk4xhh7FzfdqN+QWzpQUsEzbv+Ry1aMbhRbWxHk3Vev4O5dbfQ45ZPPIDnyyOiyn+XX2O3RR+IBklElNgtrWWeO01a+avR5jLF9SIrkj345D7S6pV79rXDmhfyd9+F/ggc/C7vvyN85Z5vek6MzSPrbYKQvt3NGI3D6uUSPJYCL3g5b3gQP/QMcfyK3858Pnv4f2PF9cGJwZsdMX01WtLLPkXqQiIiIyHmjzB2d23XUbssb0h46TkV9vMQGYGX/szwZ28Dde86NPq5mcVYlNk8f7aS+Isiqxqpxh7336hVUlwV48awzuQyS/g7o2JMIigDUL7MNFo88zInOQYyBRUnZMrWlhrW+OMXtIAAAZSdJREFUU+xn2fjzVTerB8l06TkJ334jHHpgpq9k5rTtBIz9t//u/Jyz6yg8+WX7tld+dr4Z6YfhnkSJ3zw32JnrJJuz+yA8ODpAYgy88t+gbgn86H2jfifOOSeehl99KlFm2fbizF5PlrSyz0Es5hCOOupBIiIiIucHf9CWn4T6wV9qm69mqzwpQNJzkkD3EY7XbOWeXWMyLGpbMpTYeAGSKp4+2sm25Q0YY8YdVlse5H1Xr2RvlyE6NIkxv8fcBWByXxSwZUDHfsPJzgEW1ZaPvvnVeYhSwjw50Dz+fNXN46bYfOw7z/LJHxXHndKicfYAfO1mOHgvPPu/M301M6dtF8xbbRfkB/IUILnvb8EXgCWXj5rmdF7xArK1SSU2kHuZjdegNTlAAjbQ/Iav2+DpHR/PbzlUsRg4Cz98F9QshDffZoPQrQqQnPdC0RiAMkhERETk/OH2IaG83t4NzVZ5vZ1i4zjxqTCV62/ghZM9nOkZShxX22KDCpHQ+HO4U2zOhf0cPTfIpcvrxx/jevfVywkFqvBHBm2qezaOPAIlVbDwwtH7V1wDQ1342nfRUj8mKNRmyxp+09dE33B49GNVozNITnQO8vMdZ7jrxTPEYnNwUVQIp5+Hr98CkWFYvA1OPDXTVzRzWl+E5s2w9ha7OO9vz+18J56GXT+Gl/webH4DdB+3U3LON97UrHiAxC37y7VR66nt9vdlw8rxj7VcAjf8Ney5A7Z/I7fXKTaxKPzovTZI8ub/tf83NG1JlIjNclrZ52AkYgMkpQqQiIiIyPnCC5BUTKK8Bmw5TjRkU86PPAzlDWy77GoA7t2dlGVRsxhwoC/FKEw3g2RHmw14jG3QmqymLMjmlUsB2HkkzdjgsY4+AkuvtJkyydySm8Xdz7AkqUErAG27iBk/B53F7DkzpmdBdbNN3Q/bANAPt9vr6B2OcLBjloxijcWKd6LG0Ufhm6+ymUzvuRsueLPtJZFiTHR73zDn+kdm4CKnyXAvdB+zk5TW3mT3Hbhn6udzHLjnL+zI25d8HJbbn9XzsszGyyDxSmxKKuzb+cggWbQVfGnWglf+Hqy6Hn75STj869xeq5g8+Fk4/BC88l9h0UV2X/MW+/n2GnHPYlrZ5yCkAImIiIicb5IzSCbDO36w0wYill/N6qYaVjZWcndymY13FzdVmY0bIHn6TIiyoI/Ni2ozvuSl65cDcNuDWZS09LXB2f02W2Ss2sXE6leyObxj1AQbwAZIGlYTIsjOU2PKeardspu+VqIxh9ufOcGqxkqAeJPZGXf3p+Ar1870VUzevl/Ct98ANYtscGT+alhymX3sxJPjDv/o/z3LH/4gj41LZ5v23XbbtNlOTalemFsfkj132M/j9X8BpVXQuAEq5tmf3fNNzynA2O8lT8NKOHdw6ucMDULb7vHlNcl8PnjD1+xrffdtcHz89+15Z+9d8Mi/wtZbYevvJPY3b7aNWtv3zNy1ZUkr+xyoxEZERETOO7kGSE4/Z6fUuH0+bt7UzBOHO+kZdMtT4gGSFFkfboDkiRMjXLSkbsK/scqr7WvuPHyCF09O0IvEW/gtTxEgAfoWXsnlvr0sqS8Z/UDbbgILt9BYXcqu02MawiYFSB49eJbTPcP84Y1rmV9VwjNHO5lxw73w7G3QuiP3cozp9ML34XvvgAUb4d2/TEwfadpix0SPWWhGYw47Tvbw7LGu87e0yevf0LzZlr6tvRkOPZi6VG0ikRDc+2kbFLnonXafz2ezSI48cv71zOg9CVVNozPH5q3KrUlr6w5wopkDJGAz8W79KVQ3wf+90ZaMna/OHYKffMiWML78n0c/1rTZbltnf38mrexz4GWQKEAiIiIi541Sd5LNZAMkXknO7p/Z7YqXAnDTxiaiMYf797plNl6ae2/qAIljfDx/ZpDLMpTXjL3W5tIRPn//gczHHn3EHt98QcqHT9ZeQo0ZZF3sSGLncA/0HIemTWxeVMOu02MzSBbabX8rP3j6BPUVQW7c2MQly+p55tgsyCB58Ye25Ang5DMzey3ZeuK/4ScfgOVXwbvugMp5icf8Advb4cTo8alHzw0wEonRPxLh8NmBab7gadK2047h9n5+1twMoT44/tjkz/XM16DrCNz0/+zn1LP8Gvtz6U2xOl/0nEoE2TwNq2zPpKlOmYk3aN068bHVzXDrHTb4/L+vK4osikkLDcIPbrXBuzffBsGy0Y/XLbO/f9tmfx8SrexzEA+Q+P0zfCUiIiIieZJrBsn+X9m7tfPXAHBhSx1NNaWJMpuSCtuvJE2JTTRQQcwxGfuPJK7VBkhet6Ga+/a0jS+BSXbkEVj2ktELwiS7S23gZGnP9sTONq+sYRObFtVyoL2f4XBSP48qm0EycO4k9+xu5bUXL6Y04OfS5Q0c7xykvXd44o+hkJ79FsxfB74gnHx6Zq8lG6e2w6/+DNa/Ct7+QyitHn/Mkstts8eRRI+Xfa2J3jAvnuqehgudAW277F14r3HyypfaSVOTLbMZ6oJf/yOsvA5W3zD6MS+76nwrs+k5mQgseeKjfqfYqPXUdqhpSWSRTaRuiQ34+UvgttfkPmJ4NolF4Wcfsd+jb/gfqF8+/hifD5o2FUWjVgVIcqAMEhERETnv5BogCfXb8hp3IefzGW7a2Myv93cwFHKDC7WL05TY9DNsyvEZuHhp3cSv6WaQ3LCynOqyAF94IE0WSe9p2yAwTXkNwP6BSg45i6g883hip3e3s2kTmxfXEI05oxbjVDSAL8ihQwcJRx3evG0JAJcss5+LGc0iOf0cnHkBLn2fbZBYDAGSQw/a7au/MP4OtGfJFba04VQikLX3TC8+A2VBHzsmKrWaDpEQbP8mPPd/+TlfLGaDdc2bE/tKKu3P2f5fTe5cD/8LDHXDTX83fkpV4zqobJyWRq3n+kf4h1/uYSRS4AbCjmObtHqlfZ55q+12qo1aT23PLnskWcNKuPVnEIvAt15tpwYVu1gM7vw47PoJ3PgZWHNj+mObNtvfqbHY9F3fFGhln4NQ1P5AK0AiIiIi540pT7FJCqi4/Uc8N21qYjgc45EDHXZHTUtiskSy8CC90VI2Lqqhuiw4/vE011oRG+DdV63g7l1t7DnTO/44b8GXqkGr60TnEDtLLsQcfxyibr+Utl32NWoWs8ltGDuqD4kxONXNtJ8+xgUttWxYaAM2mxbVUhb08fRM9iHZ/i0IlNnJLy2XwqlnZ/80m+OP274jmb73WrYBZtS43z2tfayYX8mWxbUzGyCJReH578B/boM7fx9++af56efRdcSOwG7aPHr/2puh8zCczbLZaOcReOorcNE7bNBsLGOmrQ/JXS+e4cu/Psxjh84V9HUY6rJlZmMzSOqXg/FNrVHrwDlbhtSybfLPXbAefucntjzqttdAX+vEz5mtHAd+9Ul47tvw0j+Dqz6e+fjmLTaA3n10Wi5vqrSyz8FI2Cux0adRREREzhNTzSAJlkOg3L49JlPjipXzqCkLcI837re2JWUGSWykn65IgG3LsgzOeP1Shnt5z1XLqSpNk0Vy5GH7cY1dYCY53jnIydpL7B/wXiPFpLKGlvpyasoC7BzTh2SwZD5lw+3x7BGwN88ubKlj+0xlkIz0w4u3w6bXQXmdDZCEBxKTUGajWNQ2X116ZebjyutgwYZRfUj2tvayfmENWxbXset0D5HoNN+hjsXsHfT/ugJ++mF7jRe9w34v9ZzI/fxJmUyjrHHH/WabRXL/34IvYCfXpLP8GjuCu3OKpSdZ2uNmYj1b6J8RLxA7tgdJoNT+HppKqcvpZ+12ogat6Sy8EN7xI9s4+bbX2Mlfxej+z8BTX4YrPwbXfmri470MqFleZqOVfQ5GNMVGREREzjfxAMkkM0jABlVql46rQQ/6fbxsYxO/2HGGA219drEy3D2qjwTAQF8P/U4Zl63I8rUDJTZLYribuooSfvcly7nrxdbRZTBgeyosuxp86fvGnegapK/5Cvf4h+2it31PfFFqjGHTotpxk2yOhGpo8vXw6osWjdq/bXk9u073MhiKZPexQP7u2u/6sb1DvfVd9n3vTvdsLrNpfdFe87KXTHzsksvgxNMQs41ZT3QOsaG5mguX1DIcjnGwo3/ic+SD49geIF95Kfzwd21Gwptvgw/8Gi52R5zmoyFn60577gUbRu+vX2Yn0RzIog/JiadsEOclvzd63O1Y09SHZK+b6VXwIKLX66imZfxjDaumVmJzarv9eiy8aOrXteRSeNv3bCDq9ndDdBK/J2aDh/8FHv03uOTdttnv2HKtVBZstJ83byLTLKWVfQ68HiSlCpCIiIjI+WLxJbbPw9i71dlYeS1c/I6Ufyz/6c3rqSz186Fvb2e4wp3+MqbMZmigj0GnlG3LJ5G9smCD7fdw6AHee/UKKkv8o7NIuk/YdPgM5TU9g2H6hiPMW7DY/hF/5BE7vSbUN+rzsGlRDXvP9MYzFIZCUV7sLmOxv5uaMSVB25Y3EI05PH+8O7uP48gj8Lll+elLsN1tzrrUDfjUL4eK+bN7ks0xdxpLVgGSK2CkBzr2xoNh65tr2LLYBvd2nJimMpsfvx++82YY6YXXfRk+/BhsfI39/l+w3h6TjwBJ206Yt8ZmaY219mb7uRvO8DGP9MFPPmjLTF4yQRnE/DW2yfKRwgVIYkm9fJ4/0V3YjB9vWtbYHiRg+5CcOzz5wOSp7dC4Hkqrcru2FdfAq/4dDj8E9/51bueaTk98CR74O7jgLfDKf8suOAL2+3fe6lk/yUYr+xwoQCIiIiLnnfpl8N67J9+DBOB1X4JrP5nyoebaMr7wtq0cPTfIF591p7uMKbOJDPdhSipZUJ2mQWcqb/2uHSH5f2+m/vAd3PqS5fzixTMcbHezSLz+IxkatB7vtKNwlzRU2ONOPGmbnAIsSARINi+uZSQS41CHHSX7y51nOBGppSLWD+GhUefcurQeYybRqPXRf7OL/qTeGlPSuhNOPQOXvCuxcDHGltnM5gySY7+xgZxM2Q2epZfb7Ykn2NtqMxHWNVezfF4l1aUBdkzHJJveM3aM8rb3wMeegQvfOjpDqbzejoHu2Jv7a7XtTB+wXHuLbfp56IHUjzsO/PwPbZDwDf8z8aLeGPszcLRwfUhOdA2yMnyA+6v/hjXhfewdm/GVTz2nbFlR1YLxj81bZX/mBifRB8VxptagNZ2L3wmXfwie+CI8/938nLOQtn/L9h1Z/yp4zX/Z6TST0bxFJTbnM02xEREREcnelavm8We3rOPHXl/EpACJ4ziY8ACV1bWTO2nNQnj3Xbbs4kfv5WMV91Ie9POFB9wXOfqILRdasDHtKU50eQGScntXNzwIz95mH0wqa9i0yPY88cYJf//pE0Qrm+yDY5ot1pYHWddUnV2j1va9iQVurhkHz37LjhK94K2j97dsg7P7bdPK2cZxbIPWpVlkjwDUr7DTVk48xd4zfVSVBmipL8fnM2xeXMuL09Gode/P7fbyD4E/TUPhxvW5fz2He2xWUXOa/jktl0JZHey/J/Xjz33bBnKu/fPssnPANmrtb5taA9Ms7DnTxx8EfsSq8H5uK/kcR3c+VpDXAWyWWvWi1OV1Dd6o30l8nN3HbEBlqv1HUrnp/9mg1J2/Dye3T3z8THnxdnuNq18Gb/x62pHpGTVtttl5Q915v7x80co+ByH1IBERERGZlPdfs5KLNm0g5hhOHkuUwhzq6KfcGaautm7yJy2vg3f+GNa/isoH/pL/WfwL7nzhFIc6+m2pwPKrMt7pPJGcQbLsKsDYgEX9ilF33Fc2VlEW9LHrdC9Hzw7w5JFONq1bax9MMY3ikmX1PHe8m2hsgjvxT/43+Ettn4RcGqmGh2DH92HDq6Fy3ujHWi6121OzcAF2dr9ddGa7gDcGllwOx59gX2sf65urMW62zAVLatlzpi9+I7Ng9twJ89fa0bjpLNgAHftyG2vatstum1JMnQG7SF1zIxy4Z/zrtO+Fuz5hp0pd80fZv6Y3herIw5O/3iy0H36eG/zPEbnoXQyaSq578gOF60vRc3J8g1bPPC9AMok+JN7PTz4DJP4gvOlbUN0E33/H7J1sc/ef24/7zf9rm9xOhTc9aRaX2Whln4N4Bomm2IiIiIhkxRjD5960lU5fPc++uIu2Xltu8/TRLsoZYf68eROcIY1gmW2Qecm7ecmZ2/jn4Ff5/q8esncrl/9Wxqce7xykriJo+4hUNCT+iB9T1uD3GTYsrGHn6R5+8MwJfAZecpF7Z7/vzLjzblteT/9IJF4GktJgJ7zwPbjgTTYLJpcAye6f2YyDS941/rHFWwEzO/uQTKb/iGfJ5dB1hPbW46xfWB3ffcHiOkLR2PhGvfk02GlLtzb8dubjFmyAyFBuY03jAZIMPYHW3AyDZxPTVcAGy25/N5RUwuu/mrFB8TgNK23WRYEata488E2GKSFw46f50vL/j/5Y0E5zaSvAlKWek+NH/Hrqltrym8k0aj31rG0MnSEjbUoq59lyweEe+P7vQGQkv+fPVX+HzSra/HooqZj6ebzfrbO4zEYr+xyoxEZERERk8qrLglQuWE5jrJ2P/N+zhCIxnj3SRqmJUFc7yRKbZD6/bXp47ad4g+8hfvfg79n9GRq0ApzoGmJJfdIf/d4d9BRjgTcvqmXP6V5+9OxJrl23gPkLl9sH+tvGHeuNK844qePZ2+wi+vIP20VX19Fx032ytv2bdnGbqt9KabU9/2zsQ3LsMdsYtGFl9s9xG9CuC+1hXXNNfPcFLW6j1kL2Idl3FzhRm6mTSaNbntWeQx+S1hdtP5NMvVlW32CngySP+/3VJ22w7fVfhurmyb2mMbbM5uij+e9D0tfKZX338mTty6FyHstWb+ItI39O1ATgtldDx/78vVYsBr2n02eQ+IO2f9FkM0gWXpi+rCoXzZvhtf8FJ5+CX/xxwXrATIkXuB07SWmyqppsw+i22TvJRiv7HKjERkRERGRqyuctZUt1P9uPdfH3d+1h51GbgWFKcpwMYQxc+0l6X/ZPNNFFv7/O9oLI4GTnoO0/4vECJCn6PmxaVEPfSIS23hHevK3FLl79JSkzSFrqy2mqKeWZo2kCJNEIPPVVG9Bo3gxN7l3pjn3ZfKSjdeyzfTy2viv9VImWbTaDJJeSj0I4/rjNHsl2GgbAwguJ+kq4xLefDc2JDJKW+nLqK4Kj+5D0nk7fo2Mq9txpx1kvvDDzcV75TS5ZQW07baAu0+emosFO9vECJDt/bINlV/2B7RcxFSuugYGOqX0vZhB6/L8JOFGOrfldwJahHXUW8uhV3wAMfOu3JxewyGSgA2Lh1CN+PfNWZf960Qicfj6/5TVjbXodXPMn8Nz/2t8Ns4XXSyfXzBlj7O+6WTzqVyv7HIyEo4BKbEREREQmrbaFquE23vOS5XzzsaN0d3Xb/SWVeTl9zdUf5JurP8/vhT5Mz3Ak7XGxmMPJriHbf8Sz+kbbE2Dty8cdv9kdJTuvsoTr1zfZP/irmlP2DTDGsG15A8+ka9S69047hvSKD9v3vbuz7buy+yCTPXubLRe46O3pj2m5FIa7J1dSUGjdx6HnRPYNWj2BUtqqNnCJbz9rkwIkxhi2tNTxghcgcRz44bvhO2+CBz6b+135kT7bn2bDb08c0CmrgdolU59kE4vahWmKTKZx1t5kF53HHrONNFsug+v/cmqvCzaDBPJbZjPSj++Zr3N3bBsLV9qSoY0LaygN+Hi4sx7edaedyPPNV0Hn4dxfLz7iN00GCdhGrZ1Zjvrt2GOzvQoZIAG47i/s755ffRKOP1nY18pW+27b7LqqKfdzNW+xWVXR9L+XZ5JW9jkYicYo8fviTaFEREREJEu1LRAZ4lPXLeDS5fVUGHf0b54CJADbrn0ND4a3cNeL47M7PG19w4SisdElNj4fbHptyikNa5qqqC4L8OZLlySyiKub0jZW3LasntM9w5zqHhr/4BP/bVP8195i369bDsGKyU8+iYzA89+B9a9MPc7Us+Qyu811lHA+TaX/iGtXYCNbfEeo8UdH7b9gcS372/oYDkfttJkTT9gmpw//E9z9F7kFSQ7cA9HQxP1HPI3rp15i03nETlRKN8Emmfc99O032MDNG7+WWxlI/QqbeZHPAMnz/0cg1MNXI69kvRvUKgn4uLClzpahLVgPt/7MBiG+9Wo7ojcX3vPT9SABm0ESHsiuMarXvydfI37T8fng9V+xZXHPfquwr5Wt9j02eyQf696mLRAdgXMHJj52BihAkoNQJEapymtEREREJs9dtAT7T/Pf77yEP71uid2fxwDJBS21rF5QxY+2n0x7zIlOG7gYlUGSQWnAz/1//FL+6Ma1iZ3VqTNIAC5dbvuQjMsiOf2cXbhf/sFEA02fzy6o2yaZQbLnThjqtOU1mcxbA6W1s6sPybHHoKx2Sqn7jwytoIQInHl+1P4tLbVEYw67T52D+/4G5q+DDzxoR/I+8UX4+R9Mvcxoz51QuSARbJrIgvV2Ss9U7pZ7fRqyySBpXG+bjoYH4dX/ad/OhTG2zOboo/kpyYpG4PEvcqxyCwdKNtJSnyhp27qsnl2ne2xAq3mzDZL0t8NvPp/ba/a6AZLaJemP8SbZZJNVdWq7LamrX5HbdWWjrAZWXmuzlfLdiyScIlibieO4AZIc+494vIDfLC2zKejq3hhzizFmnzHmoDHmk2mOebMxZrcxZpcx5jtJ+//J3bfHGPMfxk3TMMY85J7zefdfhjB5YYUiMfUfEREREZkKL+295xTzqkq5eY3beySPARJjDG/Y2sIzx7o4enYg5THH3RG/S7MMkAAsqC4jmFxiXb0Q+lMHSNY3V1NR4h/fqPWJ/4aSKrj4naP3N22cfAbJc/9rF8Qrr8t8nM8HLZdkPclmKBSd+KBcHXsMll6ZcQxzKiORKL/sWWbfOf7EqMcubKkDIPTkN+HcQbjxb202xS2fg6v/yPbn+OmHJh+0CA/bXibrX5n9VJjGDfZuedeRyb0W2ECZ8U/YQwewAY3r/xpu/nvYOEHz2Gwtv9qOX55qiVCyvXdC9zF+EHwN6xdWj8rAv2RZPeGow4un3LKohRfaDJ0d35v8Yj5Zz0k7caaiIf0xDZMY9XvqWVteM13VA6uut72N8vH59+z5OXxuKXQdy/45PScg1Je/AMn8tbZv01wLkBhj/MAXgZcDG4G3GWM2jjlmDfAp4CrHcTYBf+DufwlwFXABsBm4FHhp0lPf4TjORe6/9kJ9DBNRgERERERkiry7uj1udkfYBiryGSABeO3FizAGfvxs6iySE52DGAOL6sqm/iJVTXY8Z2hw3EMBv4+Ll9bxdHKj1r5W2PkjuOgdNnsi2YKNMNAOA2eze+3wsA0ybHh1dkGGlkttj5MJJuUcPTvAhZ+5h0cOdGR3HVPR32HT7JdeOemnHmzvpyNWTX/VsnElQ001pSyrirFp/3/BsqsS5SfGwMs+Ddf/Fez4Ptz+u5Mbp3r4QVuOkW15DST1lZlk0AvsKNT5a+wI62xc8Ca48qOTf510vGlIuZbZOA785j9wGlbx7a7NrE+aOgQ2QAJjpj1tvdX+TO25c+qv23vKZqplCmjUttjF+rmDmc810m97kBS6/0iyVW7A89AD+Tvnbz5vS8S80rZseN+7mUZNT4Y/6GbKzc5Rv4Vc3V8GHHQc57DjOCHge8BrxhzzfuCLjuN0ASQFOxygDCgBSoEgMH522gwLRRUgEREREZmSivl2YeI1Ugy5C/ZgfgMkC2vLuXr1fH707ClisfGp6ie6BmmuKaM0kGVGQCrVC+02TRbJtmUN7GvtpW84bHc883XbjPLyD44/2Cs1yXbyyZnn7YIn2yBDy6XgxGyJTwa/ePEMoUhsdGAn3457/UeumvRT957pAyDWcjmceHJUGYIxhj+u/BXV0S648e/GL5B/609sNsmeO+F7b88+S2HPnTaglWqMcjreJJupZAF4E2xmSv0ym5l05OHcznPsMTj9LF0Xvp+ekRgbFo4OkDRUlrByfuXoaU/Lr7GlLNtz6MHRczJzg1awmUD1KyZuCnvmBftzM50Bkrqltizu0IP5Od/p5+wIYYDTz2b/PO93UTaZTNlq3jL3MkiAxcCJpPdPuvuSrQXWGmN+Y4x5whhzC4DjOI8DDwJn3H93O46THHb9hlte81fGpA4JGmM+YIx5xhjzTEdHYSLfoUhME2xEREREpsLng5pFiUaKIbcEJs8ZJABv2NrCqe4hnjwyfprMyc6hrPuPpFXdbLd9qe/nbVteT8yB545324yFZ74Oa25K9D9I5gVI2rIMkHjlJUsuz+54b4E3QR+Su3fZYM+Btr7szjsVxx6HQPnE43JT2NvaS0nAR+Xqq2Dw7OgSid4zvLzvdu6MXsFAY5pzX/FhePUX4OD98O03psz+GSUahn132ekigZLsL7Sk0jbineyo36FuW9qQr7v2U7X8Gjj2m9z6kDz2BaiYx3P1dirU+oXV4w7ZuqyeZ4934XiBLp8Ptv4OHHsUzk6Q3ZFOz6nMI34981ZPXGJzarvdLipwg9axVl1v+8BMJtMpnSe/YgPQzRckPp5stO+xmTjldblfg6dpsx3DnOZ35kya6dV9AFgDXAu8DfiqMabOGLMa2AC0YIMq1xtjvFDtOxzH2QJc4/77nVQndhznK47jbHMcZ1tjY2NBLl4lNiIiIiI5qF2SKLEpYIDk5k3NVJUGUpbZHO8cHD3BZiriAZLU03IuXlqPz7iNWnf+yC4MrvhQ6nNVLYCKedkvqE88afsoVGX5925Fg10QZuhDcqp7iB0nezAG9hc0QPIbWHLp5AIOrr2tfaxtqsK/9Aq740TSONSH/h4/Uf4p8hZ2en0tUtl6K7zhf+x1/PwPMzfDPPooDHVNrrzGs2Dj5CfZeI16m7dM/vXyafk19uOeyuhpgI79sP+XcOn72dVhM6jWNY0PkFyyrJ7OgRBHzyUFqi56h+3B8txtk3/daMRmdE2UQQIwb6XtEZMpCHRqu83oyPbnLF9WXW+n+ozpszNp/R2w83a46G2w8qU2eyMSyu657bvz13/E431ft82+LJJCru5PAcktg1vcfclOAnc4jhN2HOcIsB8bMHkd8ITjOP2O4/QDvwSuBHAc55S77QO+gy3lmREjCpCIiIiITF3N4sSkiXiApCrvL1Ne4ucVW5q568UzDIYSjTlHIlHa+oZZ0lCe4dlZ8Eps0kyyqSoNsGFhjQ2QPPFfNlU9XUNVY9wFdRYBEsexgQEvSJCtlkttBkmagMDdO+3H8YotCzl6bpCRSAGatQ732BKSpZMf7ws2QLK+ucY2fCyrsxOBwN7tfu7bDF/0Hk44TYnGn+lseSNc+ynbEPTp/0l/3J477QjmVddP/mIXrLc9LqLh7J/j9WeYyRIbsI1aAR76HAycm/zzH/9P2yj1svezt7WXZfMqqCwdPz47ZR+S6mbbP+b570zucwc2WOnEMo/49TSsgshw4nfRWCP9NkAxneU1nuVXgy+Yex+SZ79pS/Eu+4D9OKKh7HqARCM2yJX3AIk3yWb29SEp5Or+aWCNMWaFMaYEeCtwx5hjforNHsEYMx9bcnMYOA681BgTMMYEsQ1a97jvz3ePDwKvAmbss6oSGxEREZEc1C6G3tMQi9oAiS8wpWyCbLxhawsDoWi8dATgVNcQjjO5CTYpldfbfippepCAHffrP/G4vXN7+YcyN45c4E6ymWi857mDdspItuU1npZtthFs9/GUD/9qVyvrm6u5aWMT0ZjDkTQTgHJy4im7gF02+QDJ2f4ROvpGWN9cbUsxllyWaNR676ehpJqKl32SRbVl7Dg5QYAE4Lc+AWtuhl99alzDV8BmFuz9Oay5EUqm8L3SuAFi4ewmpXjadkJ5QyI7aabULYHr/gL2/wr+c5sNVmQ7dra/HV74Hlz0dqicz94zfWwY06DVs7qxiuqywPhpT5e8y2Zc7fvl5K47PuI3mxIbb5JNilKeSAh+cKv9ebk4ZeFCYZVW2Z/vXAIk0TA8/XU7NrhxXaJMKJs+JJ2H7RSmBXku9Sqvt+VPs7APScFW947jRICPAXcDe4AfOI6zyxjzGWOMN3vqbuCcMWY3tufIJxzHOQfcDhwCXgReAF5wHOdObMPWu40xO4DnsRkpXy3UxzCRETVpFREREZm62hZwotDfZgMkBSiv8Vy6vIElDeX8aHviLrE34jfnHiTGQFVz2gwSsHfIX8f9REpq4IK3ZD7fgg22aW2aAEacl3Y/lQwSSNmHpKNvhKePdnLzpmbWLLClEPvbMk+8mZJjv7EBMe9aJmFfqy37iTf7XHK5bYK6+w44cDdc80dQ0cAFLXUTZ5CADbK8/ss2YPeDW+3CPtnJp+336IYpjs+NT7KZRB+S1p32Lvt0jZTN5KV/Ch982E7U+emH4Vu/bbMK0ulrhcf/C257rc1UuOKjDIWiHDk3kLL/CIDPZ9i6tJ5nxwZIVt0A1Yvg2USZzc5TPZzunqCxrle6l00GybzVdts5JoAVi8HPPgKH7off/jysvmHicxXCquugdYctk5mKvT+HvtM2MAu2VKhivh1bPBHvezbfGSRgy2xm4SSbgq7uHce5y3GctY7jrHIc57Puvr92HOcO923HcZw/chxno+M4WxzH+Z67P+o4zgcdx9ngPvZH7v4Bx3EucRznAsdxNjmO8/uO40zDgPbUQpFYbh3PRUREROYyr4Fiz0k7PrUA5TUen8/w+otb+M2hs/HF1Ykuu825BwnYO/1pepAAbGsp50bfdg7Nv37iLASvMedEo2FPPGHvxM5bM7lrXbDJNkdNESC5b08bjgO3bG5mZWMlPlOgRq3HHodFF08pI2OvGyBZ1+wutr0A0U8/Yr+n3OlAW1pqOXJ2gJ6hLMozyuvhLd+2zVF/+G5bWuDZc4fNEFpz06SvFbBlQMaX/SSbWNR+7ZtmuP9IsqZN8O5fwav+P7tY/++r4MG/t2OmwfYp2f4tGzz51/Vw96fAH4DXfgnmr2ZfWx+Ow7gRv8m2Latnf3vf6K+XPwAXvxMO3gfdJzjY3s8bvvQYf/XTCRbW8QySLAIk1Qtt+dS5pEk2jgN3/zm8+EO44dO2X81M8cq6Dj80tec/+WXbKNj7/jUGFm/NMkCyBzCJaUz51LwZzh7IforUNFH6Qw5CkSilyiARERERmRpv8dJz0maQBPMQqMjg9VsX4zjwk+fs4ulk5yAlAR8LqktzP3l1U8aJDAvbH6XaDHE3WZSUeOM0J2qMefxJmz3hm+Tfo/6AXSClCJD8amcry+ZVsL65mrKgn+XzKvPfqDU8ZJteTqG8BmDvmV7mV5Uyv8r9ui3aarNRQn1ww19B0PaUuaClFiBzo9ZkzVtspsCxR+G+T9t9jmP7j6y8FsrSL+4zCpbZUbITBbw8nYdtY86ZnmAzls8H294NH3sGNr4Gfv2P8KWXwHffBv+8Bu78uJ0c89I/hY8+bbNOLnobYL9mABsXpv8cXrKsHseB5090j37g4ncCEH322/zxD19gJBLjN4fOMhzOcJ+85xSU1tixzBMxBhpWjs4gefTf4MkvwRUfgav/cOJzFNLCC2251VTKbM7sgOOPw2XvtyONPYsvsQG7kQl+ttt3289NMMc+Tak0bbYZhNn+XEwTre5zEFKJjYiIiMjUef0Bek8VvMQGYNm8Si5dXs+Pnj2J4zgc7xykpb4cny8PZQzVCzOW2LDzR/T567jtzFJae4Yzn6usBmqXZl44DJyDcwcm33/E03KpXTyFE9fSMxTmsUNnuWVTM8Yt7VjTVMWBfJfYnNpue3Lk0KB1Q3KpRkkFtFxmF5Jb3hzfvWWxXRxn1YfEc+Fb4NL32+aiu35ieyR0H5va9JpkCzZkvxD0+jI0z3CD1nSqFtjpP7/zE5sZc/o5m7Xz/gfh97bDdX8OjWtHPWVvax+VJX5a6tMvtC9cUofPwPajY8Zx1y+DVdcx+OQ3ePFEJ2+6pIXhcCzl2O64npPZldd4GlYmepBs/xbc/xn7vXTTZ2e+zMnntwG6Qw9k3//F89SXbeDZDTLFLdoKOHDmhczPb98NTRsn95rZik+ymV1lNlrd50BNWkVERERyUFYLJdWJDJIClth43rC1hcMdA7xwsocTXXkY8eupboaRHggNjn8sNAD772Z4zasYihre8KXHONQxQdBhwQZoy9CzwhtrO9n+I56WS22QonVHfNeDe9sJRx1u3pxoDLq2qZqj5wYy362frGOPAQaWTj64E4057G/rsw1ak73tO/CuO0dl09RVlLBsXgU7TnZP7kVu/nsbcPnpR20mgfHBuldM+lpHWbDBzQwZmfjYtp12vK2XSTRbrboefu8Z+KM9cPNnbVZSmmDC7jO9rGuuzhiMrHSnPW0/3jXusePL30j1SBt/vOoUf/fazZQGfDy0rz3FWVy9J7Mrr/HMWw1dR2H3z+DnfwCrXwav/a/JZ2cVyqrrbRPoyWRbDJyDF2+3PY/K60c/ttht1Hpqe/rnh4fs9+yCAgVI6lfY3/mzbJLNLPmKF6eQxvyKiIiI5KZ2cVKApLAZJACvuGAhpQEfP9p+khOdQ7lPsPFUuUGFVJNs9v0SwoM0XvE2vveBKxkOR3nTfz/OC2NLCZI1bYSz+9OPNz3xhB3/uejiqV1vyza7TSqz+dXOVppqSrmopS6+b01TNTEHDnfkcZLNscds+cjYRVsWjp4bYCQSY93YXhbl9SnLKbYsrp1cBgnYSUpv/pbNTNn1E1h2FVTOn/S1jtK43pYTnD0w8bFtu2zfkkAeSr+mwwQZFo7jsPdML+szlNd4LllWz/PHu4lEY/F9I5EoH3m6mS5qeH/Fw5QF/Vyxch6/3pehaWnPqcllkMxbBbGI7T+zaCu8+TbwB7N/fqGtcseCT6bM5tlv2fHFl31g/GOV822z1kx9SM7ut5OmCtGgFWzwacHGWTfJRqv7HChAIiIiIpKj2pakEpvC9iABqCkLcvOmZn7y3Cl6hsIsachTbb03jjVVmc2un9gAytIr2dJSy+0ffgkVJX7e9tUneORAmkXego3uaNgUo0fB9h9ZdNHUewNUN9syHjdAMhSK8tD+dm7e1DzqLv/aJpvVc6A9T31IohE7SnfK/UfsdYzLIEnjgpZaTnUPca4/i8yNZDWL4E3ftEGozW+Y5FWm4C0ys2nU6k2wOU+c6RmmdziSmDqUwSXL6hkIRdmX1Pfm3+89wM72YfrWvYmSQ3dDfzvXrmvk8NkBjp9LkbEVHobBs9mN+PU0uKN+562Gd/xwWoK1k1LbYoNm2QZIohF4+muw/Jr0JTKLL8kcIPGyVQqVQQL2+7xt1+RLhwpIq/scjChAIiIiIpKbmunNIAF4wyUt9I/YKSV5yyBJFyAZ7oED98Km18WbJK6YX8mPP/wSljZU8J5vPs0dL5wef75Mo2EjI7bvw1T7j3hatsHJZwD49f4OhsMxbtnUPOqQFfMr8ftM/hq1tr5gJxYtvXJKT9/b2ovfZ1i9ILtyrC2L6wCyG/c71vKr4Y/3wdZ3Tf65Y81bYxvJtu9m+7EufvsLj/JUqh4ag522PKTp/AmQ7HEbtG7IIqi1danNKvLG/W4/1slXHj7EWy9dwtKXfdBmeTz/Ha5dtwCAh/anKLPxJthMJoOkZRtc+ynbV6WiIfvnTadV19vsq/AEPYwA9t1lv4+80b6pLNoKPcfTjw9u22WnNzWsnNr1ZqN5iy1NnGik+TTS6n6KYjGHSMxRDxIRERGRXNS2wECHHRM6DT1IAK5ePT8+uaYlbz1IFtrt2ADJ3rsgOgKbXz9q94KaMr7/wSu5eGk9H//uc3zjN0dGP2/+WtuHIlUfktPP23NOtf+Ip+VS6DkBXUe5e1crdRVBLlsxenFYGvCzfF4F+/PVqHXHD+12ihkke870sXJ+JWVB/8QHA5sX12DMJBu1Jqucl58+FIESm6XQvpefPHeSF0/18PavPsFtjx/FSb57/sL37Hb51bm/5izhjWVem0WApKW+nAXVpWw/1sVgKMIf/eAFFtWV85ev2mhHzS69Ep69jRXzKlg2r4KHUpXZTGbEr8cfhGs/ObnnTLdV19vpRieemPjYp75iM8TWvTz9MYsvsdvTabJI2vfA/HWFLTVaeJHdPvrvEItlPHS6aHU/RSG3Lq40qE+hiIiIyJR5afCRoYKP+fX4fYY3XNJCwGdYOi9Pr1leb++29p0ZvX/Xj+1CpeXScU+pLQ9y23su46aNTfztnbv5l7v3JRbLgVKb7p+qKaO3QMo1g2TDqwBD9NnvcN+eNm7c0EQgxc2/tU3VHMhHBsmBe+3o1Evenci4maR9bbbZZ7aqy4KsnF859QBJPi1YDx17ePJwJ5etaOClaxv565/t4hO377BNcMPD8JvP27IIr0fMeWDPmV5a6supKZt4oW2MYdvyerYf7+If7trL8c5B/uVNF1JVGrAHbL3VjuM99huuXdvIY6nG/fZ4GSSTKLEpBsuusiVfE5XZtO2Co4/Ape8dPdp3rIUX2gbE6cps2vcUrv+IZ9HFcOXHYPs34Efvya6JcYFpdT9FIxEbIFEGiYiIiEgOktPgp7Hu//dvWMNPP3pVVou2rBhj+4z0tyX2DXbaxcym16ZtZFkW9PNf79jKW7Yt4T8fPMif3r6DsNegsmkjtO8a/6TjT9q096oFuV1z3VJYdT3h7bcxMBzils2pgxZrmqo51jmY2ySb3tPwkw/a0pFb/mFKp+gbDnOicyirXhbJLmip49GDHXzk/7bz/923n1/tPMPhjn6isWnue7BgI07nEU60n+O6dQv46q3b+PgNa7h9+0ne/OXH6X7sa7bJ70v/dHqvq8DsWObsv2Zbl9ZzonOI/33iGO+5agVXrJyXeHDja6G0Fh75V65d28hwODa+VKn3pN3WLMr94meT0iqbNZYpQDLcCz//QwiU22DSROdrXJ96ks1wj/08FjpAYoydgHTj39leTd9+g33tGaTV/RSF3ABJqXqQiIiIiExdciPFaSqxARuY2Lx4/NSTnFQ3j84g2XOn7ZkwQZPPgN/H596whY/fsIYfbj/J+297hoGRiG2O2HXU9mfxOI4d8bskx/Iaz9ZbKRs8w40lu7hqdepJLWubqnAcONieVGbzwGdhxw+ye41oBG5/r82QeNM3p9xY1uuDkm2DVs97rlrBb61pZPfpXj5//wE+9O1nuf5ff83Gv/4Vr/rCI3zqxy/md4xxOo3rMTisMqe5YmUDPp/hj25cy1d+5xJOdHQz/OC/0td4ic0gmUZneoZ47zefpr03i94WkzQcjnK4oz+r/iOeS5bZPiSrF1TxiZvXjX6wpAKu/0s49ABXdf6YkoBvfJlNzykob5iWps/TbtV1dupLf4reK4OdcNtrbMDjdf+dXS+VRVttic3YJqntbjPhQjZoTXbVx+F1X4bjj8M3Xpm62fU00ep+irwSGzVpFREREclB8l3e2TY5YrKqm6EvKYNk149tpsfCCyd8qjF2sfz3r9vCw/s7eOtXnqCneo19sD1p8sm5Q3ZCx9Icy2tc0bUvp4saPlTzm7R9PdY22cVtfJLNscfg4X+CH78fHvz7iSdQ/PpzcPwxeNW/w/w1U77WPd4Em0lmkGxpqeUrt27joU9cx+6/vYU7PnYV//zGC7j1ymVUlQb47lPHuWd328QnypV7N35L8PSo4NxNm5q5+9qTNHOO3zt9E998bExfkgL72iNHuH9vOw/sTbHoztGBtn5izuS+ZlsW1/K7L1nOf7794tTfk5e9H9bcTMkDn+ZNLT08tG/MdfecnNwEm2Ky6nq7PfzQ6P0DZ+Fbr4a2nfCWb9ustWws3gqD56D72Oj9XuZaugk4hXDhW+Ft34fOw/C1G+FsmgleBabV/RR5GSQKkIiIiIjkIFgOFW7mQrHf8a1uTtz57G+HIw/b7JE05TWpvP3ypXz11m0caO/jQ/cM2Z3Jk2zi/Ufyk0Gy/eQAP4xcw4UDj6WdZrF8XiUBn0k0an3kX+3X7MK3w6//EX76EYiEUr/AoQfh4X+Bi98JF74lp2vd19pHdVmARbVlUz5HeYmfC1rqeNO2JfzFKzfyf++7gvlVJdy9a/J3rO984TTbj6WYRJNOw0rCBLi67izB5DL9aJgFL3yR6MKtBNZcz9/cuTv1ZKMCGBiJ8P1nTgDw3PHuvJ9/T6s7wWYSAZKA38ffvHoT65vTPMcYeM0XoayWP+n7R06d7Ro97rf31PkbIGm+0GbHJJfZ9LXCN15hR4K//fuZG7OOtXir3Y7tQ9K+x2b01S7J6jSf/cVu3vvNp7N/3XTWvAx+906bNff1m1KX/xSYVvdTFA+Q+LProC0iIiIiaXiTI6axxKYgqpvtyMrQIOz+GTgx2PT6iZ83xg0bmvju+69gf2geQ5TQfui5xIPHn4CyOpi/lkg0xq7TPfzfk8f42fOnONc/+QaHv9rZyk+c6/H9/+3dd3iU15n38e+tjgoSEpIAIdGbKEJ0FzDghhvGTuy499iOk2yyaU6cbIp3k9036yTrZG3HDu7d64pb3BvYgDGIDgaJKglJNDXUdd4/ZgQSqI5GEjC/z2Wu0TzzzDNnDhp8zj3n3LerhdXPNntOWEgQQ/pGeRK15mXB1vfhlDtgwf2e0qirn4FnLvPkP2istABevtVTfeS8P3a4bUfbtKeE0f1isA4EnNoSHGScnZ7Mx5sKO7TNprSyhh//32rufr2ZKkMt2FdRz9b6/owLyW36wJrn4eBOguf8nIeum8qIpGge/CSnW1aRvLwql9LKWgbERrBq1wG/X39jfgm9QoP9V067QXQiXPIAfcpzuCvk6ablfotzO1bi90QSFOTZZpP9oWfl1sFd8Oh5nqDQNS8eWWHSXkljITj82EBEQ4LWdnzW1uUWs3CxZxXSnmI/bNNKmQw3v+f5/8FjF8KW9zt/zQ5QgMRHWkEiIiIi4icN31Ke6Ftsor1JTsv2wLqXIXGMz0vUM9P68OIdp7M9KI0t65bzwcYC9pdXU751CdkRY7ly4XIm/O5dLvjrYn75yjp+8FwWk//jfc6/9zP+8+2NLN7STHWPozjneGf9HgaMyPCsSFn5RIvbZUYmx3hWkCz+M4T3hqm3eCZPs38OF98P2xd7J2p5VNXWUV1d49mCU1XqyTvSyb/b2rp6NuWXtryqoBPOGduP8uo6vsje1+7nvLu+gOraelbvLia/uKJdz1m+bT9b3ED6V28/crCu1rPCpn8GjDiHoCDj5tOHsCG/pEPt8YVzjseWbGPCwFi+NTWNLYVllFTW+PU1NuWXMrJfDMFB/gtqHTb8LDjle1wf8h4HVi3yHKsq9QQpj+dyvZ01bK4nGfSmNz0rR8r3wbWv+FYaOiQM+o2HvEZBWOc8lXDakaDVOcfdr2+gl3cr1DHbnXyVMMwTJEkY1nyi6i4U0q2vdhKpqvX8D0cBEhEREZFOavi290QPkDSUrs1d6Uk2OOeuTl1uSN8oKsZMJXnDP5n0+AriKCUrIodXaqdSllzLZZMHMmlQHzJT+3DgUDWfbSnisy17eWTxNh78JIfwkCCmDYknfUBvDMPh8P6Hc46yqjpyD1bwg7NGQPB18NodnhUqg045pi0jkqPZtH4FbsMibOaPIKJRgtvMqz3v/YXrYeFZ3BN3N31z3+e2uk9g/v/6pRLGG2vyKa2q5YyRiZ2+1tFOHZZAdHgI76zfw5zR7asM9PqaPHpHhFBSWcu76wu4/tTBbT5nac4+4i2V8LIvoKrMU0Vk3YtwYBt86+nD39YvyEzhnnc384/Pcji1hcS5/rB4616yi8r58+UZJMaE4xys2VXM6SP885rOOTbtKeHcsb6VdG6XM39NftY7XFvwRyr3f4OIWm+enJOtxG9jQ+d4bp+/xlNe/PpFMGCi79dLmQyrnoL6Ok9Z4PIiqNjfrgStb63dw/Lt+/n9JeO478OtfLS5kCumpfnelsZikuHm9z0lz7uRAiQ+qlaZXxERERH/aPi2N/RED5D099wu+zvgfNpec7ReKePotf5ZfjazL0MqC2AtfP+Ga/nJ8KbfFqclRJKRGsf35o6gvKqWZdv28dmWvSzespelOfswDO9/mIFhmEFqfC/OSU+GkAXw9p2eVSTNBEhGJsdwe9DruOBwbMYdxzZ0+Jlw41vUP30ZP9jxPXpRyVtBsxg+4GJGdrIP6usdD3yczcjkaOa2M4DREeEhwcwZncR7Gwr4/SWuzdUO+8urWbxlL7fMHMr7Gwv457o97QqQLNu2n3mJY2AfULTZM6n99B5P6eNR5x8+LyI0mGtnDOYv73/N1sJShid1rGpPez22ZDt9o8O4YEJ/qmrrMYOsXQf8FiApLK3iwKGaDpdl7pCQcHbO+RsT3pxPxQvfJuLMH3mOn8wrSGJTqEseT1BZAXbda51PpJoyCZY/6PmdTE73rB6BNgMklTV1/OGtjYzp35srpqaxIa+EV1flUlVbR3iIn9JQhPqeb8hXCpD4qMpbxSY8VAESERERkU4ZOtuzxeNET6zYsIJk95fQbwL0Hd75a3onP3ekV8PWHRAUSvigKa0+JSo8hLmjk5k7OrkDLxQG47/pyYdx3n81XSECpEceJCV4CdlpVzIiqoUJdP8JvD71SUZ+cBMpsWH8seJWSv6xjCdvnsbYAb6XVP5wUyGbC0r5y7cyCOqKrRrAuWOTvUlXDzBtSOvlUd9am09tvWN+xgCCg+Dvn+RwoLyaPlFhLT5nf3k1m/aUcs3Mid4AyUbPypF9WzxbkIKazimumZHG/R9vZeFn2/ivb0zo/Bs8yva95Xy4uZDvzx1BeEgw4SHBDEuM9mui1g35npw0HS3L3FETJk7jD69fx7/vWQgfehMJn6w5SLxuqvkZveMj+Zs/qsykTPbc5n7l+femcKPnfhsBkn98mkPuwQruuSyD4CBjzqgknl62kxXbD7RYMvxEoNm9j7SCREREJHCZ2Twz22xmW83s5808PsjMPjCzNWb2sZkNbPRYnZllef8s6t6WH6f6Z8DN75z4VWx69YFg7yR5XOdXjwBHJimFG2DXMk9fhfbyz7WPNuk6qDkE61465qHUjQtxwDuxl7V6iRe21PP9mHuJ+ZfPeez2uUSEBHHlQ0tZveugT01yznH/x1tJievFhRMGtP0EH80elURYSFC7qtksWp3H8KRoxvSPYd7Y/tTVO97f2HqZ4OXbPPlExqSPh5AIKNjgWT2SOBrGXHzM+QnR4Xxj8kBeXpVLUWnHk++25YkvdhBsxjXTj2yHyEyNY9Wug35LDrupoSxzF+SNaaxXWDA7Bl/OZyEzIH81YE3Lh59kNu0p4ZO8YN7YWkVBiR+SosYP8+QVyvNWsinc4KlSFd3ydrY9xZXc/3E288b245RhCQCcOjyBsJAgPuqCctHdSbN7HzUESMKVg0RERCSgmFkwcB9wHpAOXGlmR3/Vdg/whHNuAnA38J+NHqtwzk30/pnfLY2W7mF2ZBWJH7bXABCd7CnrmZflyW2S5p/yvs0akAnJ4z3bbBorLSB41ZN8EDaXlQdb3gZVWFrJF9n7OD9jIBYaweC+UTx/2ynERYZx9cJlrNjegZK4Xsu37WflzoPcdsbQpqVx/Sw6PITTh/flnfV7Wg0Q5BdX8OX2/czPGICZMS6lNylxvdoMrCzN2U9EaBDjUxOg70hPzoeijTDzJ8esHmlw8+lDqK6t58mlOzr13o5WVlXL/63YxQUT+pPU+8gWhsy0Puwvr2bn/kOtPLv9thSU0q93BLGRoX65Xmtmj0ri+2U3UhvV37PVLbjrXxM8Abznv9zJ08v8+3fUmtey8ggyTy7VRVl+KAcdFOT57DdUsmmoYNOKP/5zE3XOcdf5R86LDAthxtAEPvRXolY823i6o5pTY5rd+0hVbERERALWNGCrcy7HOVcNPAcc/RVwOvCh9+ePmnlcTlbxwyDtFOgzyD/XM/OsItn4OtRVQeo0/1y3pdeadJ2nokX+miPHl94P9TUsT7mOrwtKW3z622v3UO/goowj396nxkfy/G0zSIoJ57pHlne4MssDn2STEBXGZZNTO/x2OurcscnsPlBxeGtIc95YnY9r9B7NPGWCP92yl/Kq2haftzRnH1MGxXvmDkljPJVWEoa3utJoWGI0Z41J4qmlOzpUgrgtL6/cTWlVLTcclTclMy0OgCwfV/scbWtRGcOTuqd09+xRiRwkhjcn3g+XPtQtr1lX7/jd6xu486W1/PKVdbywYleXv2Z9vWNRVh6zRiaSMTCWV1bltv2k9kiZ7Mk9UlMBRZsgeWyLp67aeYCXV+Vyy+lDSEtouupvzqhEcorK2bGvvNNNyjtYwYL7lvDAJ9mdvlZHaHbvo+o6BUhEREQCVArQeCS823ussdVAw8znEiDGzBK89yPMbIWZLTWzBV3aUul+33gYrnjGv9dMTodqb2AitQtXkABMuAyCw2HVk577FQfgy4chfQF9Usew+0AFh6qbDwS8vjqP0f1iGJHcNOdE/9hePHfbDFLienHDo8v59OuidjVlfV4xH28u4qbTh9ArzE9JH1tx1phkggzeWd/ydplFq/MYnxLLkL5HVtLMG9eP6tp6PmnhfR08VM3mglJmDPXmNkkc7bmd+WNP1ZBW3DJzKPvLq3l5pX8mwvX1jsc+305GahyZaX2aPDYyOYbIsGC/5CFxzpFdWMawxO5JvDykbxRp8ZG8lhsDQ2Z2+etVVNdxx9Nf8djn27nptCHMHNGXu15ey+fZe7v0dVfsOEDuwQoWTExhQWYKG/JL2Lyn5aBlu6VMgvpaT+ng6rIWV5DUe4NCiTHh3DHn2BxLc0Z5kih/vLl9n/GWrN1dzIL7lrD7QAXjOpG/yBea3ftIOUhERESkFT8BzjCzVcAZQC7Q8BXwIOfcFOAq4H/MbFhzFzCzW72BlBVFRZ0bbEo3ikqAyNaTfHZYw2Slz2BP6cuu1KsPpM/3JGutqYDlCz3BmZk/ZmSyZzXA1sKyY56We7CCFTsONFk90lhSTATP3TqDoYnR3PLECpZsbXsi+cDH2USHh3DNDD+txmlDQnQ4UwbH824L22W27S1nbW4x8496j1MHxxMfFcY/1zX/vGXb9uMcTB/qjZFmXAFzfwXjL2+zTdOHxDM+JZaFi3Oor+/8VoPPtu4lp6icG5upuhMcZEwYGMuqnQc6/ToFJVWUV9d12woSM2P2qEQ+z97r19U2zdlXVsVVC5fy7oYCfnNROr++KJ37rp7EkL5R3P7kV2QXHfv58JdXs3LpFRrM2enJXDhhAMFBxqtZfgieNSRqXfWU57aFBK2vrc4la9dBfnbuKKLDj633MrhvFEP6RvFhJ/KQvLehgMsf/ILQ4CBe+s6pzOqC0t6t0ezeR1W1ng+eVpCIiIgEnFyg8Xr/gd5jhznn8pxzlzrnMoFfeo8d9N7mem9zgI+BzOZexDn3kHNuinNuSmJi9w4Q5TiT5F3u3tWrRxpMug4qi2H1s57tNSPnQb9xh1eGfF1w7ATwzTWeXAgXTujf4mUTosN59tvTGZIQxbefWMHKVibi2/eW89bafK6ekUZsr+7JJwFw7th+bNpT2uwWgUVZeZjBhRlN32NwkHHWmCQ+2lR4+EvUxpbm7CMiNIgJA73fhPceALN+CsFtFxQ1M26ZOYSconI+8kNuh8eWbCMxJpzzxzf/95SZ1of1eSWdDjI0BNGGJXZPgAQ8qxcqa+pZvq3juW7aa9veci594HM25JXwwNWTufG0IQD0jgjlkRumEhYSxE2Pfcn+8mq/v3Z1bT1vrc3n7PRkosJDSIwJZ+aIvry2KrfzwbPeAyC6H+R87LnfsMqpkUPVtfy/tzczYWAs35jUcsWxOaOS+CJnHxXVHfsdcs7x8OJt3PrkCkYmR/PKd09lVBdXQGqOZvc+Ug4SERGRgPUlMMLMhphZGHAF0KQajZn1NbOGQcIvgEe8x/uYWXjDOcBpwIZua7mcmJLHepK1jj6/e15v0OnQZwi8/XOo2O/ZCgIMio8kLDiILc3kIXljTT4TBsYyKKH1LRVxkWE8efM0EmPCueGR5WxsId/Hg5/mEBIcxM3eCWh3OSfds0Ln6KSrzjkWrc5l6uB4+sceW0Vo3rh+lFbVNrvFYlnOfial9SE8xLdtQueP70//2Aj+8VmOT89vsG1vOR9tLuLq6WktzmEyU+OorXeszyvu1Gs1rKLorhUkADOGeqqodHZ7R0u+2nGAS+9fQmllLc/eOoN54/o1eTw1PpKHrpvCnuJKbn1ihd9Xsnz6dREHD9WwIPPICqZLMlPIK65kuQ8JkI+RMhlwEJsKEcdWHvr7x9nsKankNxelt1pue87oRKpr6/kip/3bjWrr6vn1a+v59zc2cE56Ms/degpJMRFtP7ELaHbvI22xERERCUzOuVrge8A7wEbgBefcejO728waqtLMBjab2ddAMvB77/ExwAozW40neet/OecUIJHWhUfDT76G9G7K9RsUBJOu9SSFHTzzcGLYkOAghiZGHZOodfvectbsLuaidpbhTeodwVM3TycqPIRrH15GzlFbEgpLKnnpq918c/LAJlVWukNqfCTp/Xsfk4dkY34p2UXlx2yvaXDqsL5EhQUfE1gpPlTDxj0lzGjYXuOD0OAgbjxtMEtz9rMu1/fAxeOfbyc02LiqUWnfo030JmrtbB6SrYVlxHhXOXSXXmHBzBiawFtr8yk+VOPXa7+1Np+r/rGU2F6hvPydU5l0VP6WBpPS+vDnyyeyYscB7nxpjV8rsLyalUt8VBgzRxxZUXh2ejKRYcG86o9krSnexYzNbK8prqhh4eJtXDihP5MHtb6FcNqQeCLDgtu9zaasqpZbnljBk0t3cOusoTxw9eRuyTnUEs3ufVRVV09YSBBmLUfPRERE5OTknHvLOTfSOTfMOfd777FfO+cWeX9+0Tk3wnvOLc65Ku/xz51z451zGd7bh3vyfYi0KPNa6Dce5v5bk8Mjk2OO2WLzhnd7zQWtbK85Wmp8JE/ePB3n4JqFy8g9WHH4sYcXb6O2vp7bZg3txBvw3blj+7Fy5wEKSysPH1u0Oo+QIGtxa0pEaDCzRyfx3oYC6hptd1i+3Zt/ZEjn8tJcMS2N6PAQn1eRlFbW8OJXu7lwwoBWv5lPiokgJa4XqzpZySa7qIxhSdHdPlf64Vkj2FdexQ+fX+WXnC0FJZV895mV3PH0SsYO6M3Ld5zG4L6tr5K6YEJ/fnruKF7LyuMv72/pdBvAE0R4f2MBF4zv36TcdWRYCPPG9uPNtfmdX7HSkIekmQSt/7diF4eq67j9jGZTZjURHhLMacP78tGmojYDRMWHarjs71/w2Za9/OGS8dx1/phWV6d0BwVIfFRdW0+4Vo+IiIiIyMkoOgluXwxp05scHpkcTe7BiiYlbV9fnc/UwX0YEHfs1pPWDE+K5vGbplFaVcs1C5dRVFpF8aEanlq6gwsnDGhzu05XOXdcMs55kkWCZ3vN66vzOH1EX+Kjwlp83ryx/dhbVt0kt8rSnH2EhwSRkRrXqTb1jgjlW1NTeWNNPnmNgkntsX1vOT98LouyZkr7NiczLY4sP6wg6c78Iw0mpfXh1xeN5aPNRdz7ge/Bidq6eh5ZvI0z//QJ720o4Ednj+TZW2e0+vff2B2zh3H5lIH89YMtvLxyt8/taPDOuj1U1tQ32V7TYEFmCqWVtXzUicSoAKRMgcQxMPysJofrvJWPpg2OZ1xK+yrKzBmVRO7BimYTOjf2m0Xr2FJQysPXT2l1ZVN30gzfR9W19co/IiIiIiIBpSFR6xbvxGfznlI2F5S2WL2mLeNSYnn0hqnsKa7k2oeX8b8fbaG8nd9Ud5VRyTEMSog8vM1m5U5PadW2thDNHpVIWHBQk2o2S3P2MSmtDxGhnd8ycONpgwG4/pHlPLNsZ5MgVXP2lVXx20XrOevPn/BFzj7unDe6XYGazLQ+5B6soLCkss1zm1NSWUNhaVW35h9p7JrpaXxz8kDu/WALH25quWRzS1btPMDF9y3h7jc2MHlQH97711n8y5kjOpRDxsz4jwXjOWVoAne+tKbTwYtXs3JJje/V7NaeU4clkBgTziud3WYT0Ru+u/SYMskfbCxg94EKbvD+/rXH7FGebUCtJRZ+e20+r2bl8b25w5ntLQ98PNAM30cKkIiIiIhIoBl5uJKNJw/JG2vyCDI4b1z7t9ccbcrgeB68djI5ReX847NtzBmVSPqAY5NEdhcz49yx/fgiey8llTUsysojPCSIc8a2XmI5JiKU04Yn8M76PTjnKK6oYUN+CdOH+qfs88A+kfztykyCg4y7XlnLjD98wG8XrWdrYdOcMBXVdfzvh1s4478/5smlO/jW1FQ+/ulsvjO7fUGnid4giq/bbHKKPBWAhiX2zAogT3BiHONSevPD57LYvvfYikTNKT5Uw12vrOXSBz5nb1kV9189icdunOrzSqawkCD+fu1kRvWL4fanvmo2gW97FJZWsmTrXi7OSGl2y1JIcBDzMwbw0eZCDh7yf/WcR5dsJyWu1+EExu0xIK4Xo/vFtJiHpKi0il++uo7xKbF8d85wfzXVLzTD91F1nQIkIiIiIhJY0uIjCQ/xVLJxzvHGmnxO8X6D3RmzRiby1yszSe4dzr+cOcJPrfXduWOTqalzvL+hgDfX5jN3dBIxEW2XG543rh+7D1SwIb+EL7d58o90JkHr0c4f35+3fzCTF28/hbljknh62Q7O+vOnXPWPpby9Np/nv9zJ7Hs+4p53v+bUYQm888NZ/P6S8R2qCDJ2QG9Cg83nRK2HS/z20AoS8OSEeeDqyQQFGbc/9RWHqltebVNbV89TS3cw908f89zyndx02hA++PFszh/fv9M5VGJ7hfLETdMZlBDJLY+v4KsdHa8288bqfOodXDyx5RVMl2SmUFPneHNtfmeae4yN+SV8kbOPa08ZREgH00vMGZ3Eiu0HKKlsmjDXOcddr6ylrKqWP1+e0SSnyvHg+GrNCaS6tl4VbEREREQkoAQHGcMSo/m6oIz1eSVs21ve7uo1bZk3rh9Lf3EmmS1UCOlOmal9SIwJ50/vfs3esuoWq9cc7awxyQSZJ2fEsm37CAsJOrwiw1/MjCmD47n3iky++MWZ/PTcUezYd4jvPL2SO19aS0pcL168/RQeum6KT9tcIkKDSR8Qy6pGuVQ6IruojNBgIy0+0qfn+0tqfCR/vSKTzQWl/OLltcckDHXO8cHGAubd+xm/enUdwxKjef37p/NvF6YTHR7it3bER4Xx1M3TSe4dwQ2PfMna3R2rRPTa6jzS+/c+vL2tOWMH9GZ4UrR/qtk08vjn24kIDeKKqakdfu6cUUnU1juWbGm6cubllbm8t6GAn54zqtX31FM0w/dRlbbYiIiIiEgAGpkczZaCUl73VnaZN66f3659vFSIDAoyzk5PJvdgBdHhIcwZ3b4cCQnR4UwZHM876wtYmrOfzNQ4v+QfaUnf6HC+O2c4n/5sDo/eMJVHb5zKS985lSmDO7etJzM1jjW7i6mtq+/wc7cWljEoIeq4WBkwa2QiPznHU1Hm0SXbDx9fl1vM1QuXcfPjK6irdzx47WSev20GYwe0LwlpRyX1juDpW6bTu1co1z6yjM17Stt+ErBtbzmrdx1sNjlrY2bGJZkpfLn9ALv2H/JHk9lfXs0rq3K5JHMgcZHtS07b2KS0OHpHhDTZZpN3sILfLlrPtMHx3HT6EL+00996/rf2BKUcJCIiIiISiEYkx5BXXMnLq3KZNTLRp8nTieDcsZ7AzzljkzsU5Dh3bD82F5SyNrfYr9trWhMcZMwZncScUUl+CTJlpsVRUVN3TEnn9sguKmN4D1Swackds4dxTnoyf3hrI6+vzuNHL2Rx0f8uZmN+Cb+bP5Z3/3UW547t1+XBuQFxvXjm29MJDwni6oXLyClqu29fy8rFDOZnpLR5bsMqp9ey/LOK5Lkvd1JVW9+uykfNCQkOYtbIRD7+uoj6eodzjp+9uIY657jnsgyCe7icb0s0w/dRdW094QqQiIiIiEiAaUjUWlRaxYUTfE/Oerw7dVgCV05L5dZZQzv0vHMbJXP1V4LW7paZ6tnmtGpXx7bZVNfWs2PfIYYl9UyC1uaYGX+6PIO0+Ei+/+wq3liTz22zhvHJz+Zw/amDu3Wly6CEKJ6+ZTrOOa5euKzV1R7OOV7LymPGkAT6xbadQyY1PpJpg+N5ZVXuMduJOqqmrp4nv9jBacMTGNXP920wc0YlUVRaxYb8Ep5auoPFW/fyywvGkJbQs9uvWqMZvo+q6uoJ60CpJxERERGRk8HIZM/qgLCQIM7uQGWLE01ocBD/eekERvfrWEWdgX0iGZfSm7DgoGbLsp4IUuN7kRAV1uFErTv3l1NX73qsxG9LYiJCefiGqXxn9jA+/PEZ/Py80fRuR9LdrjA8KYYnb55OeVUtVy1cymNLtrF5T+kxQY01u4vZtre8ze01jS3ITCG7qJx1uSWdauM76/eQX1zJDad2bhvMGaMSMfNUwvnDW5uYNTKRq6aldeqaXc1/2WcCjJK0ioiIiEggSu0TSVRYMDNHJLarsksgunPeaHKKyrs0/0hXMjMy0+I6nKh1a2FDid/jK0ACMKRvFHfOG93TzQAgfUBvnrh5Ov/6fBa/fX0DAH2jw5g+NIFThiZwyrAEXs3KJSw4iHkdKKF9wfj+/HbRel5Zlcv4gb7nU3lsyXbS4iOZ287cOy3pGx3OhIFxvLRyN70jQvjjNyYcN3mGWqIAiY+qa+u0xUZEREREAk5QkPHEzdMY2Of4XSbf02aOSGTmiMSebkanTEyN4/2NhRQfqiE2sn2BsGxvXo2hx2GA5HgzMTWOj34ym137D/FFzj6WZu/j8+x9vLnmSKnec8cmE9ur/UHI2MhQ5oxO5OVVu7ntjKEk925/eecGa3cXs2LHAX51wRi/5AmZOyqJ1bsOcvfF49q1VainKUDio+o6JWkVERERkcA0edCJmVtD2q+h3PLq3QeZNbJ9wZ7swjL6x0b4tUzuyS41PpLU+Egun5KKc47t+w7xefZesnYe5HofEqT++JxRLLhvCbc/9RXP3TqD8A6mhXj0821EhgVzuQ+lfZtz0+mDmTAwltmjToyAoWb4PtIWGxEREREROVlNGBiLGR3KQ7K1qOy43F5zojAzhvSN4urpg/jvyzIYl9LxbTIjk2O457IMVu08yG8Xre/Qc4tKq3hjdT7fnDzQbzlaYiJCmTPaP9WVuoNm+D5SmV8RERERETlZxUSEMjIppt2VbJxzZBeWHXcJWgPR+eP7c8fsYTy7fBfPLNvZ7uc9s2wn1XX1Pq1cOVlohu+jKgVIRERERETkJDYxNY6sXQfbVTZ2T0kl5dV1DEs8fkr8BrIfnzOKM0Ym8ptF6/hqx/42z9+1/xBPLt3B7FGJAb0KSDN8H2kFiYiIiIiInMwy0+I4eKiG7fsOtXludkMFG60gOS4EBxl/vSKTAXG9uP2plRSUVDZ7Xl29Y+FnOZzzl0+pqK7l+3NHdHNLjy+a4fugvt5RW+9UxUZERERERE5aDYlaV+5oe5tNQwWb4QG8+uB4ExsZykPXTqG8qpbvPPUVVbV1TR7fmF/Cpfcv4T/e3MiMofG8+6MzmDyoTw+19vigGb4PquvqAbSCRERERERETlrDk6KJjwrjo82FbZ67tbCMmPAQEmPCu6Fl0l6j+nmStq7ceZDfLtoAQGVNHX96dzMX/W0xuw5UcO8VE3nkhqmkxPXq4db2PNVf8kFVrTdAoio2IiIiIiJykgoOMs4f348Xv9pNeVUtUa2U780uKmNYUvQJU60kkDQkbb3/42xiIkL4YGMB2UXlXJqZwq8uTCc+Kqynm3jc0AzfB9XeAIm22IiIiIiIyMlsfkYKlTX1vLehoNXzthaqxO/xrCFp60Of5lBZU8/jN03jz9+aqODIUbSCxAfaYiMiIiIiIoFgyqA+9I+NYNHqPBZkpjR7TkllDYWlVSrxexwLDjL+dlUm/1y7hwsm9G91NVAg0wzfBw0rSBQgERERERGRk1lQkDE/YwCffl3EgfLqZs/JKfJWsFGJ3+Na74hQLp+aquBIKzTD98HhAElwcA+3REREREREpGtdlDGA2nrHW+vym318a6G3go1WkMgJTgESHzSUR9IKEhEREREROdmNHdCboYlRLMrKa/bx7KIyQoONtPjIbm6ZiH9phu8DJWkVEREREZFAYWZcnJHC8u37yS+uOObxrYVlDE6IIkRVPuUEp99gHygHiYiIiIiIBJL5EwfgHLyx+thtNtlFqmAjJwfN8H1QpSo2IiIiIiISQIb0jWLCwFgWrW66zaa6tp4d+w4xLEkJWuXEpxm+D44kaVX3iYiIiIhIYJifMYC1ucXkFJUdPrZzfzl19U4JWuWkoBm+D5SDREREREREAs2FEwZgRpNVJFsLG0r8KkAiJz7N8H2gHCQiIiIiIhJo+sVGMH1IPItW5+GcAzz5R0ABEjk5aIbvg2rlIBERERERkQA0PyOFnKJy1ueVAJBdWEb/2AiiwkN6uGUinacZvg+qauoA5SAREREREZHAct64foQE2eFtNluLypR/RE4amuH7QCtIREREREQkEPWJCuOMkYm8vjqPunpHdqFK/MrJQzN8HxxJ0hrcwy0RERERERHpXvMnDiC/uJI31uRRXl3HsESV+JWTQ5cGSMxsnpltNrOtZvbzFs653Mw2mNl6M3um0fE/eo9tNLO/mpl5j082s7Xeax4+3p0aAiShwd3+0iIiIiIiIj3qrDHJRIQGce8HWwAYpi02cpLosgCJmQUD9wHnAenAlWaWftQ5I4BfAKc558YCP/QePxU4DZgAjAOmAmd4n/YA8G1ghPfPvK56Dy2pqqsnLCSIHojNiIiIiIiI9Kio8BDOTu9HTpGnxO9wbbGRk0RXriCZBmx1zuU456qB54CLjzrn28B9zrkDAM65Qu9xB0QAYUA4EAoUmFl/oLdzbqnz1JV6AljQhe+hWdW19YQrQauIiIiIiASo+RkDAIiJCCExJryHWyPiH105y08BdjW6v9t7rLGRwEgzW2JmS81sHoBz7gvgIyDf++cd59xG7/N3t3FNAMzsVjNbYWYrioqK/PKGGlTX1itBq4iIiIiIBKxZI/vSOyKEYYnRWlkvJ42eLlYdgmebzGxgIPCpmY0H+gJjvMcA3jOzmUBFey/snHsIeAhgypQpzo9tZvKgPkSGKUGriIiIiIgEpvCQYP77sgyiwnp6SiniP13525wLpDa6P9B7rLHdwDLnXA2wzcy+5kjAZKlzrgzAzN4GTgGe5EjQpKVrdrlLJw1s+yQREREREZGT2Llj+/V0E0T8qiv3iXwJjDCzIWYWBlwBLDrqnFfxBEMws754ttzkADuBM8wsxMxC8SRo3eicywdKzGyGt3rNdcBrXfgeRERERERERCQAdFmAxDlXC3wPeAfYCLzgnFtvZneb2Xzvae8A+8xsA56cIz91zu0DXgSygbXAamC1c+5173PuABYCW73nvN1V70FEREREREREAkOXbhhzzr0FvHXUsV83+tkBP/L+aXxOHXBbC9dcgaf0r4iIiIiIiIiIX6gUi4iIiIiIiIgEPAVIRERERERERCTgKUAiIiIiIiIiIgFPARIRERERERERCXgKkIiIiIiIiIhIwFOAREREREREREQCngIkIiIiIiIiIhLwFCARERERERERkYCnAImIiIiIiIiIBDwFSEREREREREQk4ClAIiIiIiIiIiIBTwESEREREREREQl4CpCIiIiIiIiISMBTgEREREREREREAp4CJCIiIiIiIiIS8BQgEREREREREZGApwCJiIiIiIiIiAQ8BUhEREREREREJOApQCIiIiIiIiIiAU8BEhEREREREREJeAqQiIiIiIiIiEjAM+dcT7ehy5lZEbDDz5ftC+z18zVPJuqftqmPWqf+aZ36p23qo9b52j+DnHOJ/m6MtE5jmR6jPmqd+qd16p/WqX/apj5qnd/HMgERIOkKZrbCOTelp9txvFL/tE191Dr1T+vUP21TH7VO/SP6HWib+qh16p/WqX9ap/5pm/qodV3RP9piIyIiIiIiIiIBTwESEREREREREQl4CpD47qGebsBxTv3TNvVR69Q/rVP/tE191Dr1j+h3oG3qo9apf1qn/mmd+qdt6qPW+b1/lINERERERERERAKeVpCIiIiIiIiISMBTgMQHZjbPzDab2VYz+3lPt6enmdkjZlZoZusaHYs3s/fMbIv3tk9PtrEnmVmqmX1kZhvMbL2Z/cB7XH3kZWYRZrbczFZ7++h33uNDzGyZ97P2vJmF9XRbe5KZBZvZKjN7w3tf/eNlZtvNbK2ZZZnZCu8xfca8zCzOzF40s01mttHMTlH/BDaNZZrSWKZ1Gsu0TWOZ9tFYpmUay7Suu8YyCpB0kJkFA/cB5wHpwJVmlt6zrepxjwHzjjr2c+AD59wI4APv/UBVC/zYOZcOzAC+6/2dUR8dUQXMdc5lABOBeWY2A/h/wF+cc8OBA8DNPdfE48IPgI2N7qt/mprjnJvYqNybPmNH3Av80zk3GsjA83uk/glQGss06zE0lmmNxjJt01imfTSWaZ3GMi3rlrGMAiQdNw3Y6pzLcc5VA88BF/dwm3qUc+5TYP9Rhy8GHvf+/DiwoDvbdDxxzuU751Z6fy7F82FOQX10mPMo894N9f5xwFzgRe/xgO4jMxsIXAAs9N431D9t0WcMMLNYYBbwMIBzrto5dxD1TyDTWOYoGsu0TmOZtmks0zaNZXyizxjdO5ZRgKTjUoBdje7v9h6TppKdc/nen/cAyT3ZmOOFmQ0GMoFlqI+a8C65zAIKgfeAbOCgc67We0qgf9b+B/gZUO+9n4D6pzEHvGtmX5nZrd5j+ox5DAGKgEe9y5oXmlkU6p9AprFM++gz0gyNZVqmsUyb/geNZVqjsUzLum0sowCJdDnnKZUU8OWSzCwaeAn4oXOupPFj6iNwztU55yYCA/F8uzm6Z1t0/DCzC4FC59xXPd2W49jpzrlJeLYMfNfMZjV+MMA/YyHAJOAB51wmUM5RS1ADvH9E2qTPiIfGMq3TWKZlGsu0i8YyLeu2sYwCJB2XC6Q2uj/Qe0yaKjCz/gDe28Iebk+PMrNQPAOKp51zL3sPq4+a4V0u9xFwChBnZiHehwL5s3YaMN/MtuNZCj8Xzz5M9Y+Xcy7Xe1sIvIJnYKrPmMduYLdzbpn3/ot4Bhnqn8ClsUz76DPSiMYy7aexTLM0lmmDxjKt6raxjAIkHfclMMKbcTkMuAJY1MNtOh4tAq73/nw98FoPtqVHefdXPgxsdM79udFD6iMvM0s0szjvz72As/Hsb/4I+Kb3tIDtI+fcL5xzA51zg/H8m/Ohc+5q1D8AmFmUmcU0/AycA6xDnzEAnHN7gF1mNsp76ExgA+qfQKaxTPvoM+KlsUzbNJZpncYyrdNYpnXdOZYxz0oU6QgzOx/PHrpg4BHn3O97tkU9y8yeBWYDfYEC4DfAq8ALQBqwA7jcOXd08rOAYGanA58Bazmy5/IuPHt31UeAmU3Ak1gpGE/g9gXn3N1mNhTPtwzxwCrgGudcVc+1tOeZ2WzgJ865C9U/Ht5+eMV7NwR4xjn3ezNLQJ8xAMxsIp6keGFADnAj3s8a6p+ApLFMUxrLtE5jmbZpLNN+GsscS2OZtnXXWEYBEhEREREREREJeNpiIyIiIiIiIiIBTwESEREREREREQl4CpCIiIiIiIiISMBTgEREREREREREAp4CJCIiIiIiIiIS8BQgEZEThpnNNrM3erodIiIiIr7QWEbk+KYAiYiIiIiIiIgEPAVIRMTvzOwaM1tuZllm9qCZBZtZmZn9xczWm9kHZpboPXeimS01szVm9oqZ9fEeH25m75vZajNbaWbDvJePNrMXzWyTmT1tZtZjb1REREROShrLiAQmBUhExK/MbAzwLeA059xEoA64GogCVjjnxgKfAL/xPuUJ4E7n3ARgbaPjTwP3OecygFOBfO/xTOCHQDowFDiti9+SiIiIBBCNZUQCV0hPN0BETjpnApOBL71fiPQCCoF64HnvOU8BL5tZLBDnnPvEe/xx4P/MLAZIcc69AuCcqwTwXm+5c263934WMBhY3OXvSkRERAKFxjIiAUoBEhHxNwMed879oslBs3876jzn4/WrGv1ch/4dExEREf/SWEYkQGmLjYj42wfAN80sCcDM4s1sEJ5/b77pPecqYLFzrhg4YGYzvcevBT5xzpUCu81sgfca4WYW2Z1vQkRERAKWxjIiAUrRShHxK+fcBjP7FfCumQUBNcB3gXJgmvexQjx7ewGuB/7uHTTkADd6j18LPGhmd3uvcVk3vg0REREJUBrLiAQuc87XlWEiIu1nZmXOueieboeIiIiILzSWETn5aYuNiIiIiIiIiAQ8rSARERERERERkYCnFSQiIiIiIiIiEvAUIBERERERERGRgKcAiYiIiIiIiIgEPAVIRERERERERCTgKUAiIiIiIiIiIgFPARIRERERERERCXj/H1q0APNfeYHxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1332x756 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.9535205960273743\n",
      "Test accuracy: 0.7162036895751953\n"
     ]
    }
   ],
   "source": [
    "show_history(story)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10: momentum and learning rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Learning rate has been overlooked for a while but currently a lot of research is looking into it.\n",
    "Lower LR takes more time but allows better allocation of local minimum, higher LR allows faster calculations but drastic jumps do not always yield good results. However, learning rate doesn't have to be remained constant. We will try different learning rate schedules for our NN.\n",
    "\n",
    "The SGD optimization algorithm in Keras has an argument called **decay**. This argument is used in the time-based learning rate decay schedule equation as follows:\n",
    "LearningRate = LearningRate * 1/(1 + decay * epoch) (if decay=0 then there is no change)\n",
    "\n",
    "Another term to know is **momentum**. Use a large momentum. Using a larger momentum value will help the optimization algorithm to continue to make updates in the right direction when your learning rate shrinks to small values.\n",
    "\n",
    "$$\\Delta  W_k(i) = -\\eta  \\frac{\\partial E}{\\partial W_k} + \\mu  \\Delta W_k(i-1)$$\n",
    "\n",
    "\n",
    "$\\mu$ is a momentum term here. Imagine if at some point gradient is zero, it would put the backpropagation to stall. Here momentum comes to the rescue - the algorithm remembers its last step, and adds some proportion of it to the current step. This way, even if the algorithm is stuck in a flat region, or a small local minimum, it can get out and continue towards the global minimum. So learning rate measures how much the current situation affects the next step, while momentum measures how much past steps affect the next step. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(layers.Dense(nb_units, activation='relu',bias_initializer='zeros',kernel_initializer=keras.initializers.he_normal(seed=seed), input_shape=(35,)))\n",
    "model.add(layers.Dropout(0.1))\n",
    "model.add(layers.Dense(nb_units, activation='relu',bias_initializer='zeros',kernel_initializer=keras.initializers.he_normal(seed=seed)))\n",
    "model.add(layers.Dropout(0.1))\n",
    "model.add(layers.Dense(nb_classes, activation='softmax'))\n",
    "# Compile model\n",
    "learning_rate = 0.01\n",
    "decay_rate = learning_rate / epochs\n",
    "momentum = 0.8\n",
    "\n",
    "sgd = keras.optimizers.SGD(learning_rate=learning_rate, decay=decay_rate, nesterov=False, momentum=momentum)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
    "# Fit the model\n",
    "story = model.fit(X_train, y_train, #name model differently\n",
    "                    batch_size=56,\n",
    "                    epochs=epochs*2,\n",
    "                    verbose=0,\n",
    "                 validation_data=(X_test, y_test))\n",
    "score = model.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.9110918045043945\n",
      "Test accuracy: 0.717129647731781\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n",
    "show_history(story)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "90/90 [==============================] - 0s 4ms/step - loss: 0.8672 - accuracy: 0.7149 - val_loss: 0.9118 - val_accuracy: 0.7116\n",
      "Epoch 2/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8634 - accuracy: 0.7147 - val_loss: 0.9128 - val_accuracy: 0.7134\n",
      "Epoch 3/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8656 - accuracy: 0.7175 - val_loss: 0.9120 - val_accuracy: 0.7130\n",
      "Epoch 4/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8718 - accuracy: 0.7177 - val_loss: 0.9115 - val_accuracy: 0.7134\n",
      "Epoch 5/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8637 - accuracy: 0.7155 - val_loss: 0.9121 - val_accuracy: 0.7144\n",
      "Epoch 6/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8691 - accuracy: 0.7119 - val_loss: 0.9117 - val_accuracy: 0.7134\n",
      "Epoch 7/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8638 - accuracy: 0.7195 - val_loss: 0.9125 - val_accuracy: 0.7139\n",
      "Epoch 8/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8591 - accuracy: 0.7185 - val_loss: 0.9118 - val_accuracy: 0.7153\n",
      "Epoch 9/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8721 - accuracy: 0.7123 - val_loss: 0.9116 - val_accuracy: 0.7134\n",
      "Epoch 10/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8626 - accuracy: 0.7127 - val_loss: 0.9119 - val_accuracy: 0.7157\n",
      "Epoch 11/30\n",
      "90/90 [==============================] - 0s 3ms/step - loss: 0.8614 - accuracy: 0.7167 - val_loss: 0.9115 - val_accuracy: 0.7157\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping\n",
    "cb=EarlyStopping(monitor='val_loss', min_delta=0.1, verbose=0, patience=10, restore_best_weights=True) #difference of loss is below min_delta, it is quantified as no improvement\n",
    "story = model.fit(X_train, y_train, #name model differently\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    callbacks=[cb])\n",
    "score = model.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.9118472337722778\n",
      "Test accuracy: 0.7115740776062012\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n",
    "show_history(story)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}